Job Applications – New Batch · April 2026

Lukas Westphal-Ostrowski  ·  PhD Candidate, Real-World Studies & Regulatory Science

Salary target: 651,100 – 956,900 DKK/yr  ·  benchmark: Novo Nordisk Epidemiology Manager (Jun 2026)

Status update. RWD Steward Manager rejected  ·  ALK Clinical Data Manager submitted ✓  ·  ALK AI Engineer: interview 🎉  ·  Novo Nordisk Senior CDM not applied  ·  Signum RWE: ghosted after call — closed  ·  NN Safety Surveillance Principal Specialist: new — apply by Jun 14  ·  NN Postdoc Cardiometabolic: new — apply by Jun 15  ·  NN Postdoc Health Economics & Data Science: new — apply by Jun 15  ·  AstraZeneca Health Data Scientist (Nordics): interview 🎉  ·  NN Epidemiology Manager (maternity cover): submitted ✓.
Total positions
17
Submitted
3
Interview
2
Countries
DK · DE

Overview

Position Company Location Deadline Status Fit Contract Link
Clinical Data & AI Engineer
Data pipelines, agentic AI, clinical data science architecture
ALK
Clinical Data Science / Global Clinical Development
Hørsholm, Denmark
(DTU Science Park)
Apr 23, 2026
✓ Applied
Interview 7.5/10 Permanent → View job
Clinical Data Manager
eCRF, EDC systems, data quality, clinical trials
ALK
Biometrics / Global Clinical Development
Hørsholm, Denmark
(DTU Science Park)
May 3, 2026
✓ Applied
7.0/10 Permanent → View job
Health Data Scientist — Medical Evidence (Nordics)
Nordic RWE datasets, healthcare data integration, observational evidence generation
AstraZeneca
Medical Evidence / Nordic Marketing Company
Stockholm, Sweden
(flex: Copenhagen, Oslo, Espoo)
Jun 18, 2026
✓ Applied
Interview 8.5/10 Permanent → View job
Safety Surveillance Principal Specialist
Clinical study safety analytics, benefit-risk, product safety profiles
Novo Nordisk
Global Patient Safety
Søborg, Denmark
Jun 14, 2026
13 days left
Identified 8.5/10 Permanent → View job
Postdoc — Precision Cardiometabolic Medicine
Computational modelling, ML, multi-cohort cardiovascular / cardiometabolic RWD
Novo Nordisk
IHI Consortium / Innovative Healthcare Initiative
Måløv, Denmark
Jun 15, 2026
14 days left
Identified 8.0/10 Postdoc
1.5 yr (Aug 2026 – Mar 2028)
→ View job
Postdoctoral Researcher — Health Economics & Data Science
CarePath consortium, HTA, treatment persistence, health economic modelling
Novo Nordisk
Public-Private Partnerships
Søborg, Denmark
Jun 15, 2026
6 days left
Identified 7.0/10 2 yr FTC
Start: Aug 1, 2026
→ View job
Epidemiology Manager
PASS, post-authorisation RWE, pharmacoepidemiology, regulatory & safety evidence
Novo Nordisk
R&D Real-World Evidence – Regulatory & Safety
Søborg, Denmark
(or London, UK)
Jun 28, 2026
✓ Applied
9.5/10 12-month FTC
Maternity cover
→ View job
Data Analyst
Marine coating performance analytics, Power BI, BI tools
Hempel
Marine Data & Technology
Copenhagen, Denmark
Not specified
Rolling
Identified 5.5/10 Permanent → View job
Analyst / Senior Analyst – RWE
RWE consulting, Nordic registries, pharmacoepidemiology
Signum Life Science
Nordic RWE Consulting
Copenhagen, Denmark
No deadline
Closed — ghosted
Closed 9.0/10 Permanent → View job
RWD Steward Manager
RWD governance, FAIR principles, data ethics
Novo Nordisk
R&D Real World Evidence
Søborg, Denmark
Apr 23, 2026
Closed — rejected
Rejected 7.8/10 Permanent → View job
Safety Surveillance Specialist
Signal detection, PASS, post-marketing PV
Novo Nordisk
Global Patient Safety
Søborg, Denmark
Apr 27, 2026
Closed — not applied
Not Applied 9.2/10 Permanent → View job
Senior COA Manager
Clinical outcomes assessments, patient-focused drug dev.
Novo Nordisk
Global Medical Affairs
Søborg, Denmark
Apr 28, 2026
Closed — not applied
Not Applied 6.0/10 Permanent → View job
Senior Clinical Data Management Professional
End-to-end trial data management, GCP, regulatory submissions
Novo Nordisk
Clinical Data Management & Programming (CDMP)
Søborg, Denmark
May 4, 2026
Closed — not applied
Not Applied 7.5/10 Permanent → View job
Data Scientist / Biostatistician
AF-BOLD data infrastructure, EU-IHI consortium, cardiovascular RWD
UKE Hamburg
University Heart & Vascular Centre
Hamburg-Eppendorf, Germany
May 11, 2026
Closed — not applied
Not Applied 6.5/10 3 yr FTC
Full/part-time
→ View job
AI & Digital Transformation Professional
Generative AI use cases in pharma Quality (GxP)
Lundbeck
Corporate Product Quality
Copenhagen, Denmark
May 15, 2026
Closed — not applied
Not Applied 6.0/10 Permanent → View job
Statistician / Senior Statistician
Danish registers, EHR data, mental disorders research
Copenhagen Research Centre for Biological & Precision Psychiatry
Mental Health Centre Copenhagen / Region H
Gentofte Hospital, Copenhagen
May 17, 2026
Closed — not applied
Not Applied 9.0/10 1 year
Start: Jun 1, 2026
→ View job
Scientist — Computational Audiology & ML
Real-world hearing data, ML models, translational research
Demant / Eriksholm Research Centre
Oticon R&D — 35+ scientists
Snekkersten, Denmark
(partly Smørum)
May 17, 2026
Closed — not applied
Not Applied 8.5/10 Permanent → View job
Senior Researcher — Child Public Health
COPSY longitudinal study, child & adolescent mental health epidemiology
UKE Hamburg
Zentrum für Psychosoziale Medizin / Kinder- & Jugendpsychiatrie
Hamburg-Eppendorf, Germany
May 18, 2026
Closed — not applied
Not Applied 7.0/10 80% FTC
Jul 2026 – Aug 2027
→ View job

Detailed Analysis

🧠 Copenhagen Research Centre for Biological & Precision Psychiatry — Statistician / Senior Statistician 9.0/10

About the centre

PI: Prof. Michael E. Benros (chief physician, PhD, director)

Team: 25–30 researchers — doctors, statisticians, bioinformaticians, geneticists

Focus: Prevention and treatment of severe mental disorders using nationwide Danish registers, large-scale genetic data, population-based EHR data (Sundhedsplatformen), and multimodal clinical data

Ethos: Robust, well-known methods applied to new settings; prioritise scientific publication over incremental predictive gains

Contract

Duration: 1 year (with evaluation for extension)

Start: June 1, 2026

Location: Gentofte Hospital, Copenhagen

Deadline: May 17, 2026

Contact: Prof. Benros (+45 26255239) or Secretary Signe Clausen

Requirements

  • MSc or PhD in Statistics, Bioinformatics, or related field
  • Strong biostatistics / epidemiology skills
  • R programming (preferred) — functions, codebase, reproducible research
  • Survival analysis, medical statistics, large-scale register data
  • Proven scientific writing for medical / non-technical readership
  • Multi-disciplinary collaboration ability

Preferred qualifications

  • Nationwide register / large-scale dataset management
  • Causal inference: IPW, g-methods, target trial emulation
  • Longitudinal clinical trial analysis, estimand framework
  • Machine learning and predictive modelling
  • Scientific publication process (writing, submission, peer review)

Exceptional matches

  • PhD in Real-World Studies + MSc Social Data Science
  • Danish nationwide register expert (core daily work)
  • R, Python, SAS proficiency
  • Survival analysis & Cox regression (exact requirement)
  • Causal inference: g-formula & doubly robust methods
  • Machine learning in health data
  • Multiple peer-reviewed publications in medical journals
  • SSRI / antidepressant research = direct psychiatric domain

Minor gaps

  • No formal psychiatry / mental health research background
  • No genetics / bioinformatics / multi-omics experience
  • No Sundhedsplatformen EHR experience specifically
  • 1-year contract vs. permanent preference

Directly transferable

  • Antidepressant (SSRI) research → psychiatric domain credibility
  • Danish register pharmacoepidemiology → their exact data infrastructure
  • Target trial emulation / g-formula → listed preferred qualification
  • NLP on literature → research network analysis for psychiatry
  • Teaching epidemiology to MDs → communicating stats to clinicians

Standout application — your SSRI research directly bridges into their psychiatric focus

Key differentiator: Most statisticians applying here will have strong methods but no content-area relevance to psychiatry. You have both: your antidepressant effectiveness and safety research is literally psychiatric pharmacoepidemiology. Lead with this explicitly — it is unusual and highly valuable.

Cover letter angle: Frame as a natural scientific progression: "My PhD research on SSRI effectiveness and safety using Danish registry data aligns directly with your group's focus on mental disorders and treatment. I already work with the same data infrastructure, the same causal inference methods, and publish in the same journals your group targets."

Highlight on CV: SSRI personalised treatment project (g-formula, doubly robust), SSRI safety cohort study, and the literature network analysis project. Explicitly list g-formula and target trial emulation in your skills section — they are preferred qualifications here.

🧪 ALK — Clinical Data Manager 7.0/10

Role focus

Team: Clinical Data Management group within Biometrics, Global Clinical Development

Core work: Build and maintain clinical databases, eCRFs, and Data Management Plans across trials in North America, Europe, and China. Hands-on data oversight: monitoring incoming data, resolving queries, managing discrepancies, ensuring clean delivery.

Growth angle: ALK is actively investing in data governance, platform development, and automation — looking for someone curious to grow in that direction alongside core CDM duties.

Contract

Type: Permanent

Location: Hørsholm, Denmark (DTU Science Park) — onsite with some flexibility

Deadline: May 3, 2026

Status: Submitted — CV + short motivation letter via EasyCruit portal

Requirements

  • BSc or higher in STEM (natural sciences, health, data science, IT)
  • 2–3 years clinical development experience (pharma/CRO) preferred
  • Python, R, or SAS — working knowledge, not developer-level
  • Detail-oriented data work: cleaning, checking, querying, resolving
  • Organised, reliable, able to manage multiple studies
  • Fluent English; good communication with sites, monitors, vendors

Key responsibilities

  • Study data oversight — own the state of trial data
  • EDC system building, configuration, and maintenance
  • Data quality control — implement data checks, manage discrepancies
  • Maintain DMPs, validation documents, edit check specifications
  • Reporting: listings, status summaries, support for interim/final analyses
  • Contribute to data standards and governance initiatives

Solid matches

  • PhD-level STEM qualification (exceeds BSc requirement)
  • Python, R, SAS — all three languages proficient
  • Large-scale health data management (Danish registries)
  • Data quality and reproducible research workflows
  • Regulatory science background — understands compliance context
  • Cross-functional collaboration experience
  • Curiosity and growth mindset (explicitly valued here)

Real gaps

  • No clinical trial / EDC system experience (Medidata, Veeva, etc.)
  • No eCRF build or Data Management Plan experience
  • No pharma industry or CRO background
  • No site query management or clinical operations exposure
  • Registry data ≠ interventional trial data in structure

Transferable

  • Registry data management → clinical database principles
  • SAP / analysis plan writing → Data Management Plan writing
  • Pharmacovigilance → regulatory compliance mindset
  • SAS proficiency → standard CDM programming tool
  • Research project coordination → study timeline management
  • Data governance interest aligns with ALK's stated growth direction

Good opportunity to enter clinical trials — lean into the technical + governance angle

Honest assessment: The hands-on CDM experience gap (EDC systems, eCRF builds, site query management) is real, but ALK explicitly says a different data-intensive background can work. Your PhD-level statistics and SAS/R/Python skills significantly exceed what most CDM candidates bring — that's your differentiator.

Cover letter angle: Position this as a deliberate move into clinical trials from a strong methodological base. Emphasise that you already work with large health datasets, write analysis plans, manage data quality, and understand the regulatory context. ALK's investment in automation and data governance is the hook — show genuine curiosity about that direction and how your programming skills are directly relevant to it.

Address the gap directly: Acknowledge you haven't worked with EDC systems specifically, but note that your data management depth and technical skills mean the learning curve is in the tooling, not the underlying concepts.

🤖 Lundbeck — AI & Digital Transformation Professional 6.0/10

Role focus

Team: Corporate Product Quality, within Production Development & Supply

Core work: Develop and implement generative AI use cases within Quality processes (documentation, deviations, CAPAs); collaborate with Quality experts, IT, and digital teams; build AI capabilities and support adoption across the function

Level: Entry-level / early career (0–2 years experience) — explicitly a graduate-level role

Contract

Type: Permanent

Location: Copenhagen, Denmark

Deadline: May 15, 2026

Contact: Paul Hansen, Project Director (PUHN@lundbeck.com)

Matches

  • Life sciences / data science degree (far exceeds requirement)
  • Hands-on generative AI and LLM experience
  • Pharma/regulatory science context — GxP awareness
  • Interest in building AI tools in clinical/pharma settings
  • Collaborative, stakeholder-facing research experience

Real concerns

  • PhD-level candidate for a 0–2 year graduate role — overqualified
  • Quality processes (deviations, CAPAs) are outside your background
  • Likely lower salary ceiling given entry-level framing
  • Role is more implementation support than engineering

Transferable

  • LLM pipeline experience → generative AI use case building
  • Regulatory science PhD → GxP mindset from day one
  • Research tool building → practical AI solution delivery

Low-effort application — worth a try, but temper expectations on level fit

Honest assessment: Lundbeck is hiring for someone early-career curious about AI. You are a PhD with real LLM project experience. They may see you as overqualified; alternatively, they may see your technical depth as a bonus. The role description mentions only "a few lines of motivation" — low investment to apply.

If you apply: Keep the motivation minimal and lean into AI enthusiasm and pharma curiosity. Don't oversell the PhD — frame it as scientific rigour applied to practical AI implementation.

📊 Hempel — Data Analyst 5.5/10

Role focus

Team: Marine Data & Technology

Company: Global coating manufacturer — marine, decorative, infrastructure, energy. Founded Copenhagen 1915, owned by Hempel Foundation.

Core work: Maintain and improve BI tools; develop Power BI dashboards; analyse marine coating performance data; conduct statistical analyses; train colleagues on BI tools; advise global clients on hull coating upgrades, sustainability, and regulatory strategies

Contract

Type: Permanent

Location: Copenhagen, Denmark

Deadline: Not specified

Apply: Via Workday portal — CV + motivation letter in English

Technical matches

  • Statistical analysis and data science skills
  • Python and R proficiency
  • Large-scale data analysis and visualisation experience
  • Analytical rigour and scientific communication

Notable gaps

  • Completely different domain — marine coatings, no life science
  • No Power BI or BI tooling experience
  • Role is primarily BI/reporting, not advanced statistics or modelling
  • PhD-level candidate for what is a generalist analyst role
  • Client-advisory angle differs from research background

Transferable

  • Statistical analysis → coating performance analytics
  • Data visualisation experience (Three.js) → BI mindset
  • Client communication → translating data for non-technical stakeholders

Lowest priority — apply only if bandwidth allows

Honest assessment: This is the weakest domain fit in the batch. Marine coatings is far from your background, the role centres on Power BI tooling you haven't used, and it sits below your qualification level. The statistical work is present but not the focus. Apply if the other roles thin out or if you're genuinely drawn to the marine sustainability angle.

🎧 Demant / Eriksholm Research Centre — Scientist, Computational Audiology & ML 8.5/10

Role focus

Centre: Eriksholm Research Centre — Oticon's R&D arm, 35+ scientists, world-leading audiological research data access

Core work: ML models for computational audiology — predicting hearing aid satisfaction and outcomes from real-world data; decision-support models for clinical efficiency; translating research data into scalable, deployable representations; clustering and population-level labelling of in-market fitting data

Data: Experience sampling studies (Eriksholm) + large-scale in-market data on Demant cloud platforms

Contract

Type: Permanent

Location: Snekkersten (partly Smørum), Denmark

Deadline: May 17, 2026

1st interviews: 27, 29 May & 1 June

Contact: Sergi Rotger Griful — segr@eriksholm.com

Qualifications sought

  • PhD in Applied ML, Mathematical Modelling, Computer Science, Data Science / Data Engineering, Biostatistics, or Social Data Science
  • Early-career researcher (within ~5 years of PhD)
  • Applied ML on real-world data
  • Solid programming, statistical, and data engineering skills
  • Experience translating analytical methods into operational algorithms
  • Research output appropriate to career stage (publications, software, etc.)

Key responsibilities

  • Develop ML models for translational audiological applications
  • Bridge Eriksholm research data and Demant cloud platforms
  • Collaborate with scientists, audiologists, clinicians, industry partners
  • Support R&D through scientific documentation and demonstrators
  • Publish in peer-reviewed journals and present at audiology conferences

Exceptional matches

  • PhD in Real-World Studies + MSc Social Data Science — explicitly listed qualifying field
  • Early-career researcher (within 5 years of PhD ✓)
  • Applied ML on real-world health data (exact framing)
  • Python, R — full ML stack proficiency
  • Peer-reviewed publications appropriate to career stage
  • Experience translating analysis into deployable outputs (Three.js network project)
  • Longitudinal and experience-sampling data familiarity
  • Cross-disciplinary collaboration with clinicians and non-technical stakeholders

Gaps

  • No audiology or hearing care domain knowledge
  • No cloud platform experience (AWS, GCP, Azure) explicitly
  • No hearing aid fitting data experience
  • Location: Snekkersten is ~45 min from Copenhagen by train

Directly transferable

  • Large-scale real-world data ML → hearing aid in-market data
  • Treatment outcome prediction (SSRIs) → hearing aid satisfaction prediction
  • Population-level clustering and subgroup analysis → audiological population labelling
  • LLM pipeline + Three.js project → translating models into operational demonstrators
  • Cross-disciplinary clinical collaboration → audiologist & clinician partnerships
  • Peer-reviewed publications → strong track record for a research centre role

Strong application — your MSc is explicitly named, your ML profile maps well, and the research culture fits

Key differentiator: Most applicants will be ML engineers without a research publication track record, or researchers without strong engineering depth. You sit at the intersection — an MSc in Social Data Science (explicitly listed), a PhD with peer-reviewed output, real-world data ML experience, and demonstrated ability to build end-to-end systems. That combination is unusual.

Domain gap strategy: The role is fundamentally about ML on real-world longitudinal data — the fact that the data is audiological rather than pharmacoepidemiological is a detail, not a disqualifier. Frame your SSRI outcome prediction work (predicting treatment response from registry data) as directly parallel to predicting hearing aid satisfaction from usage data. Both are treatment outcome prediction problems on observational, real-world cohorts.

Cover letter angle: Lead with the methodological parallel — real-world data, treatment outcomes, longitudinal population-level modelling — then acknowledge the domain shift as intentional and explain why hearing care research interests you. Eriksholm explicitly values research integrity and taking chances; that culture suits your profile well.

Note: Deadline is May 17 — same as the psychiatry statistician. Both deserve strong applications; this one may have higher upside given the research environment and the explicit MSc field match.

🫀 UKE Hamburg — Data Scientist / Biostatistician 6.5/10

Role focus

Team: University Heart & Vascular Centre — EU-IHI AF-BOLD consortium (atrial fibrillation burden, 60+ clinical trials, 200,000+ patient-years)

Core work: Design and operate data harmonisation pipelines for the unified AF-BOLD cardiovascular dataset; implement QA systems; coordinate data science team; publish findings informing clinical guidelines and regulatory decisions

Language: German + English required — German application expected

Contract

Type: Fixed-term, 3 years

Hours: Full-time or part-time available

Location: Hamburg-Eppendorf, Germany

Deadline: May 11, 2026 — 6 days

Apply: UKE Jobs portal

Requirements

  • MSc or PhD in Data Science, Biostatistics, or related field
  • 2+ years post-graduate professional experience
  • PyTorch and microservice / API development
  • PostgreSQL, object storage, Git/GitLab with CI/CD pipelines
  • German and English fluency
  • Clinical cardiovascular medicine knowledge
  • Team mentoring experience

Preferred qualifications

  • Clinical trial data analysis (CDISC standards, medical ontologies)
  • NLP / LLM methods for medical text extraction
  • Linux administration and Kubernetes
  • Familiarity with clinical trial data standards

Strong matches

  • MSc/PhD in Data Science + Biostatistics background
  • German fluency — rare and decisive for a German role
  • Python proficiency across data science stack
  • NLP / LLM pipeline experience (exact preferred qualification)
  • 2+ years post-graduate research experience
  • Peer-reviewed publications for regulatory/clinical audiences
  • Real-world data analysis and harmonisation methods

Gaps

  • No PyTorch / deep learning framework experience
  • No microservice or REST API development
  • No PostgreSQL or object storage experience
  • No CI/CD or GitLab pipeline experience
  • No cardiovascular / atrial fibrillation domain knowledge
  • No Kubernetes or Linux admin experience

Transferable

  • Danish registry data pipelines → AF-BOLD harmonisation principles
  • Pharmacoepidemiology methods → cardiovascular RWD methodology
  • NLP/LLM project → medical text extraction (listed preferred)
  • SAS / R / Python data management → PostgreSQL learning curve
  • Regulatory science background → guideline and regulatory output
  • PhD supervision / teaching → team coordination

German opportunity — language fluency and NLP experience are rare differentiators here

Honest assessment: The data infrastructure and software engineering requirements (microservices, CI/CD, PostgreSQL, Kubernetes) are significant gaps relative to your analytical background. The biostatistics core is a strong match, but this role leans toward data engineering leadership. Worth applying given the German-language advantage and direct NLP/LLM match, but temper expectations on the engineering side.

Cover letter angle: Write in German. Lead with your biostatistics/epidemiology depth and NLP/LLM experience — medical text extraction from clinical trial data is listed as a preferred qualification and maps directly to your LLM pipeline work. Frame the DevOps gap as tooling to learn, not concepts: you already design and maintain complex research data pipelines.

Deadline: May 11 — 6 days. German Bewerbung (CV + Anschreiben) expected.

🧒 UKE Hamburg — Senior Researcher, Child Public Health 7.0/10

Role focus

Team: Forschungssektion Child Public Health — interdisciplinary group of psychologists, health scientists, sociologists, and methodologists

Core work: Coordinate and extend the COPSY longitudinal study (8 waves since 2020); quantitative analysis of child and adolescent mental health, quality of life, and well-being; lead peer-reviewed publications; develop grant applications; mentor PhD students

Language: German working environment — German application expected

Contract

Type: Fixed-term (Elternzeitvertretung)

Hours: 80% part-time

Start: 1 July 2026

End: 31 August 2027

Location: Hamburg-Eppendorf, Germany

Deadline: 18 May 2026

Requirements

  • PhD in Psychology, Health Sciences, Public Health, or Epidemiology
  • Several years of independent research experience
  • Empirical research in child/adolescent mental health or Public Health
  • Quantitative methods & statistics (SPSS or R)
  • Longitudinal data analysis (advantageous)
  • Track record of international peer-reviewed publications
  • Third-party funding / grant writing experience (desirable)
  • Measles immunisation documentation required before start

Key responsibilities

  • Lead coordination of COPSY waves 2026 & 2027
  • Longitudinal statistical analyses of mental health outcomes
  • Lead author on international publications
  • Conference presentations
  • Develop new research questions and study designs
  • Liaison with national & international project partners
  • Grant applications and third-party funding
  • Supervision of PhD students

Strong matches

  • PhD in epidemiology / health sciences — exact requirement
  • Quantitative longitudinal methods — direct overlap
  • R proficiency — primary tool required
  • Peer-reviewed international publications
  • Population-based study experience
  • German fluency — decisive for Hamburg role

Gaps

  • No direct child/adolescent mental health research background
  • No COPSY or similar cohort study experience
  • No grant application track record in this domain
  • Academic public health research rather than data science focus

Transferable

  • Pharmacoepidemiology cohort methods → longitudinal population surveys
  • Danish registry studies → representative longitudinal design
  • Risk/protective factor analysis → exact COPSY analytical framework
  • RWD analytical pipelines → survey data management
  • Publication experience → lead authorship requirement

Domain pivot — strong methods fit; child mental health framing is the key challenge

Honest assessment: Methodological profile (longitudinal analysis, R, population studies, publications) is a strong match. The main gap is the child/adolescent mental health domain. Parental-leave cover means a fast hiring decision is likely.

Cover letter angle: Write in German. Lead with your longitudinal epidemiology methods and R expertise — these are the technical core. Frame domain adjacency positively: public health epidemiology methods transfer directly to survey-based child health research. Mention any relevant co-authored work touching on mental health, quality of life, or population surveys.

Deadline: May 18 — 6 days.

🔬 Novo Nordisk — Safety Surveillance Principal Specialist 8.5/10

Role focus

Team: Global Patient Safety — clinical safety evaluation during drug development

Core work: Lead analytical evaluations of safety data from clinical studies; establish product safety profiles; drive benefit-risk assessments across clinical programs; address complex safety challenges across the development pipeline; influence patient surveillance and business decisions through cross-functional collaboration

Contract

Type: Permanent (full-time)

Location: Søborg, Denmark

Deadline: 14 June 2026

Requirements (inferred)

  • MSc/PhD in Life Sciences, Medicine, or Natural Sciences
  • Pharmacovigilance / drug safety background
  • Clinical trial safety data analysis
  • Benefit-risk assessment methodology
  • Strong cross-functional communication and collaboration
  • Seniority to lead evaluations independently (Principal level)

Key responsibilities

  • Lead safety data evaluations from clinical studies
  • Establish and maintain product safety profiles
  • Drive benefit-risk assessments across programs
  • Address complex safety challenges in development pipeline
  • Collaborate with clinical, regulatory, and medical affairs teams

Strong matches

  • PhD in Real-World Studies & Regulatory Science
  • Drug safety and pharmacovigilance research background
  • Epidemiological study design and execution
  • Danish registry-based safety data expertise
  • Peer-reviewed drug safety publications
  • Regulatory science — benefit-risk assessment context
  • Python / R / SAS for safety data analytics

Gaps

  • No clinical trial / interventional study safety experience
  • Post-marketing PV background vs. clinical development focus
  • No pharma industry Principal Specialist seniority level
  • No benefit-risk framework application in clinical development

Transferable

  • PASS / NIS study experience → clinical program safety input
  • Causality assessment methods → benefit-risk evaluation
  • Regulatory science PhD → safety regulatory responses
  • RWE signal detection → safety surveillance methodology
  • Cardiometabolic safety research → relevant therapeutic area

Strong application — mirrors the Safety Surveillance Specialist role (9.2/10) that closed in April

Context: This is a more senior counterpart (Principal Specialist) to the Safety Surveillance Specialist posted in April. The focus shifts toward clinical development safety rather than post-marketing, but your pharmacovigilance and RWE-based safety research background is a direct match for the core analytical and benefit-risk work.

Cover letter angle: Lead with your drug safety publications and pharmacovigilance research depth. Frame the shift from post-marketing to clinical development safety as a natural progression — emphasise that the underlying methodology (benefit-risk evaluation, causal inference, safety data analysis) is the same. Highlight independent research leadership to address the Principal-level seniority expected.

🫀 Novo Nordisk — Postdoc, Precision Cardiometabolic Medicine 8.0/10

Role focus

Initiative: Innovative Healthcare Initiative (IHI) — two public-private EU consortia on cardiovascular and cardiometabolic disease

Core work: Lead computational modelling within the IHI consortia; independently shape the research agenda; design and apply machine learning and statistical models to multi-cohort patient datasets; translate analytical findings into clinical insights that advance precision medicine across Europe

Contract

Type: Postdoc (fixed-term)

Duration: 1.5 years — 3 August 2026 to 31 March 2028 (potential extension)

Location: Måløv, Denmark

Deadline: 15 June 2026

Requirements (inferred)

  • PhD in Data Science, Biostatistics, Epidemiology, or related field
  • Machine learning and statistical modelling expertise
  • Multi-cohort / multi-source patient data experience
  • Cardiovascular or cardiometabolic domain knowledge (preferred)
  • Ability to independently lead a computational research agenda
  • Strong scientific communication and publication record

Key responsibilities

  • Lead computational modelling within EU IHI consortia
  • Design and apply ML and statistical models to patient data
  • Independently shape the research agenda
  • Translate findings into precision medicine clinical insights
  • Collaborate with multi-national public and private partners
  • Publish and disseminate results in peer-reviewed journals

Strong matches

  • PhD in Real-World Studies + MSc Social Data Science — exact computational profile
  • ML and statistical models on real-world multi-source health data
  • Danish registry multi-cohort linkage expertise
  • Cardiometabolic research context (diabetes / SSRI co-morbidity studies)
  • Early-career researcher (within 5 years of PhD)
  • Peer-reviewed publications demonstrating scientific rigour
  • Independent research agenda ownership (PhD experience)

Gaps

  • No dedicated cardiovascular disease computational modelling experience
  • No EU IHI / public-private consortium project experience
  • Fixed-term postdoc (1.5 yr) — assess against career objectives
  • Måløv location (not central Copenhagen)

Transferable

  • Danish registry multi-cohort studies → IHI multi-cohort dataset analysis
  • SSRI effectiveness modelling (g-formula, doubly robust) → cardiometabolic precision medicine
  • Independent PhD research → lead computational direction in consortium
  • Pharmacoepidemiology methods → cardiovascular RWE methodology
  • Cross-disciplinary clinical collaboration → multi-partner consortium work

Strong research fit — weigh contract type against career goals before applying

Key differentiator: Your PhD + MSc combination in real-world studies and social data science is precisely the computational-meets-epidemiology profile this role requires. Leading ML modelling on multi-cohort patient data in a cardiometabolic precision medicine context is a direct extension of your current research.

Note on contract: This is a 1.5-year postdoc, not a permanent position. If you want to strengthen your research profile before a full industry transition, this is excellent. If you're prioritising a permanent role now, focus on the Principal Specialist first.

Cover letter angle: Lead with your multi-cohort RWE modelling experience and precision medicine research interest. Emphasise that your Danish registry work has prepared you for large-scale multi-source patient data, and that your ML/statistical methods map directly to cardiometabolic precision medicine. Highlight publication record and the independent research leadership gained during your PhD.

💊 Novo Nordisk — Postdoctoral Researcher, Health Economics & Data Science 7.0/10

Role focus

Initiative: CarePath — EU consortium of 40+ partners improving treatment persistence for chronic disease management (obesity, type 2 diabetes, cardiovascular disease)

Core work: Health technology assessments; real-world data analysis of disease burden and treatment patterns; health economic modelling (cost-effectiveness, budget impact); medication persistence and adherence research using patient-level longitudinal data; ML-driven personalisation approaches

Based: Novo Nordisk Søborg headquarters with academic partner collaboration

Contract

Type: Fixed-term, 2 years

Start: August 1, 2026

Location: Søborg, Denmark

Deadline: June 15, 2026

Salary: DKK 570,200 – 838,100 / year

Requirements

  • PhD in health economics, epidemiology, biostatistics, data science, or related field
  • Health economic evaluation methods: HTA, cost-effectiveness analysis
  • Real-world data analysis and statistical modelling
  • R or Python with version control and Docker
  • Healthcare registry or longitudinal patient data experience
  • Understanding of cardiometabolic diseases
  • Treatment adherence / persistence research knowledge
  • GDPR and health data privacy familiarity (preferred)

Key responsibilities

  • Conduct health technology assessments and care pathway evaluations
  • Analyse real-world data on disease burden and treatment patterns across Europe
  • Evaluate medication persistence using patient-level longitudinal data
  • Develop health economic models (cost-effectiveness, budget impact)
  • Contribute to ML-driven personalisation methodology
  • Collaborate with 40+ consortium partners across Europe

Strong matches

  • PhD in epidemiology / Real-World Studies — exact field match
  • Real-world data analysis on longitudinal patient cohorts
  • R and Python proficiency — primary tools required
  • Danish registry data expertise (patient-level longitudinal data)
  • GDPR-compliant health data workflows
  • Cardiometabolic disease research context (diabetes, SSRI co-morbidities)
  • Treatment outcome modelling — methodologically adjacent to persistence research
  • Multi-partner European consortium collaboration experience

Real gaps

  • No formal health economic training — HTA, cost-effectiveness modelling, budget impact are specialised sub-disciplines
  • No Markov model or decision-analytic model experience
  • No direct treatment adherence / persistence research background
  • Docker / containerisation not a regular workflow tool
  • 2-year fixed-term (same concern as Cardiometabolic Postdoc)

Transferable

  • Causal inference on RWD → persistence and adherence pattern analysis
  • Danish registry pharmacoepidemiology → multi-country RWD comparison
  • SSRI treatment effectiveness modelling → cost-effectiveness model inputs
  • Regulatory science background → HTA evidence generation methods
  • ML on patient data → personalisation component of CarePath

Solid data science fit — health economics specialisation is the main gap to address

Honest assessment: Your RWD and data science profile maps well to the analytical core of this role. The gap is health economic modelling — cost-effectiveness analysis, Markov models, and budget impact are specialised methods not covered in an epidemiology/data science PhD. Whether this is disqualifying depends on how central HTA modelling is vs. the RWD analysis and ML components. Given three NN postdocs with Jun 15 deadlines, this is likely the weakest fit of the three.

Cover letter angle: Frame as a methodological bridge: your RWD expertise (registry-based longitudinal analysis, causal inference, treatment outcome modelling) generates the evidence that feeds health economic models. Emphasise the CarePath ML personalisation angle — that maps directly to your strengths. Acknowledge the health economics specialisation gap directly and note that it's learnable within the postdoc environment.

Note: If bandwidth is limited with three June 15 deadlines (Safety Surveillance, Cardiometabolic Postdoc, this role), prioritise the Safety Surveillance Principal Specialist and Cardiometabolic Postdoc first — both are better fits.

📈 AstraZeneca — Health Data Scientist, Medical Evidence (Nordics) 8.5/10

Role focus

Team: Medical Evidence team within the Nordic Marketing Company

Core work: Build integrated healthcare datasets across Nordic countries for real-world evidence generation; data management, quality assurance, and statistical analysis; methodological contributions; clear technical documentation, visualisations, and reporting; ensure compliance with privacy and regulatory standards

Job ID: R-254077 — posted June 8, 2026

Contract

Type: Permanent (assumed — not stated)

Location: Stockholm, Sweden — flexibility across Nordic offices (Copenhagen, Oslo, Espoo)

Deadline: June 18, 2026

Status: Submitted — CV + cover letter via AstraZeneca careers portal

Requirements

  • Bachelor's in biostatistics, epidemiology, data science, health informatics, public health, or related field
  • Demonstrated experience with healthcare or real-world data
  • Data integration, cleaning, and transformation across multiple sources
  • SQL, R, Python, and/or SAS proficiency
  • Healthcare coding systems: ICD, ICD-O-3, SNOMED, ATC
  • Healthcare data privacy and regulatory understanding
  • Fluent English; Nordic language skills advantageous

Desirable

  • Nordic healthcare data experience
  • Background in observational studies / evidence generation
  • Familiarity with AI/ML applications in healthcare

Strong matches

  • PhD in epidemiology / Real-World Studies — far exceeds Bachelor's floor
  • Direct healthcare/RWD experience with Danish nationwide registries
  • Data integration and cleaning across multiple registry sources
  • SQL, R, Python, SAS — all listed tools covered
  • ICD and ATC coding systems — used routinely in pharmacoepidemiology
  • Healthcare data privacy / GDPR — regulatory science PhD background
  • Observational study design and evidence generation — core PhD work
  • AI/ML in healthcare — LLM and ML pipeline experience
  • Location flexibility includes Copenhagen — no relocation pressure

Gaps

  • No SNOMED or ICD-O-3 (oncology coding) experience specifically
  • No industry/pharma marketing-company environment experience
  • Swedish/Nordic language skills beyond Danish not confirmed as an asset here
  • Contract type and seniority level not specified — could be junior-titled despite scope

Transferable

  • Danish registry pipelines → Nordic multi-country dataset integration
  • Pharmacoepidemiology methods → real-world evidence generation for Medical Evidence team
  • SAS/R/Python data management → statistical analysis and reporting requirements
  • Scientific writing and visualisation → technical documentation and reporting
  • Cross-disciplinary collaboration → Nordic cross-country team coordination

Excellent fit — directly maps Danish RWD expertise onto a Nordic-scale role with Copenhagen flexibility

Key differentiator: The requirements (SQL/R/Python/SAS, ICD/ATC coding, registry-based RWD, observational evidence generation, GDPR compliance) describe your day-to-day PhD work almost exactly, just extended to a Nordic multi-country scope instead of Denmark alone. A PhD against a stated Bachelor's requirement signals strong overqualification in a positive direction here, since the role is data-science-heavy rather than purely operational.

Cover letter angle: Lead with concrete registry/RWD pipeline experience — building, cleaning, and integrating large-scale longitudinal datasets, applying ICD/ATC coding, and producing evidence for regulatory and clinical audiences. Note the Copenhagen-based flexibility explicitly so they know relocation isn't a barrier. Mention any exposure to Swedish/Nordic registries or willingness to learn Swedish if relevant.

Deadline: June 18 — 8 days. Posted June 8, so still fresh; apply promptly given strong fit.

📊 Novo Nordisk — Epidemiology Manager (12-month maternity cover) 9.5/10

Role focus

Team: R&D Real-World Evidence – Regulatory & Safety (R&S) — generates regulatory-grade RWE supporting regulatory submissions, health authority interactions, and patient safety decisions across the product lifecycle

Core work: Lead post-authorisation safety studies (PASS) as scientific lead; translate safety concerns and health authority commitments into study designs, protocols, and analyses; coordinate PASS activities across Global Patient Safety, Global Regulatory Affairs, Clinical Operations, and external partners; turn regulatory and pharmacovigilance questions into actionable RWE strategies

Contract

Type: 12-month fixed-term (maternity cover)

Location: Søborg, Denmark or London, UK

Deadline: 28 June 2026 (reviewed continuously — apply early)

Salary: 651,100 – 956,900 DKK/yr base

Requirements

  • MSc, PhD, or equivalent in Epidemiology
  • Experience in pharmacoepidemiology or drug safety studies
  • Hands-on real-world data experience (registries, EMR, claims)
  • Ability to translate safety/regulatory questions into study designs
  • Strong cross-functional team collaboration
  • Ability to manage multiple priorities in a fast-paced environment

Key responsibilities

  • Act as scientific lead for secondary PASS (real-world data)
  • Contribute to primary and affiliate-led PASS
  • Translate health authority commitments into robust epidemiological studies
  • Coordinate across GPS, GRA, Clinical Operations, and vendors
  • Manage vendor oversight, outsourcing, timelines, and priorities
  • Deliver RWE strategies aligned with regulatory commitments

Near-perfect matches

  • PhD in Real-World Studies & Regulatory Science — exact title match
  • Pharmacoepidemiology as core research domain
  • Danish national registry RWD experience (primary requirement)
  • PASS and NIS scientific input experience
  • Drug safety study design and execution
  • RWE-based regulatory evidence generation
  • Cross-functional collaboration (clinicians, statisticians, regulators)
  • Peer-reviewed publications in drug safety and RWE
  • R / SAS / Python for epidemiological data analysis

Gaps

  • No pharma industry experience
  • No vendor / outsourcing management experience
  • No global regulatory affairs coordination experience
  • 12-month fixed-term — not permanent

Directly transferable

  • PASS / NIS study co-authorship → scientific lead for PASS
  • Regulatory science PhD → translating HA commitments into protocols
  • Danish registry expertise → primary RWD source this team uses
  • Drug safety publications → credibility for safety evidence generation
  • Cross-disciplinary coordination → GPS / GRA / ClinOps alignment
  • Pharmacoepidemiology study designs → study protocol writing

Strongest match in the batch — your PhD is essentially written for this role

Why this fits so well: R&D RWE – Regulatory & Safety is precisely the intersection of your PhD training. PASS design and execution using real-world registry data, translating pharmacovigilance questions into epidemiological studies, regulatory evidence generation — these are the literal outputs of your PhD research. Few applicants will have this combination of pharmacoepidemiology depth, RWD expertise, and regulatory science background.

Biggest advantage: The role explicitly requires hands-on real-world data experience (registries, EMR, claims). Danish national registry expertise is your strongest differentiator — Novo Nordisk's PASS studies frequently rely on Nordic registry data, and you know this data infrastructure better than most candidates they will see.

On the fixed-term: 12 months maternity cover, but Novo Nordisk has a strong track record of converting strong maternity cover candidates to permanent roles. The network and internal visibility gained are highly valuable even if not converted directly.

Cover letter: Lead with PASS. Cite specific PASS/NIS experience from your PhD and name the Danish registries you use. Frame the role as the exact industry application of your academic work. Apply before the deadline — applications are reviewed continuously.

Recommended application order

# Position Why this order Action needed
RWD Steward Manager — Novo Nordisk Applied Apr 23 — rejected. Closed
🎯 Clinical Data & AI Engineer — ALK Applied Apr 23 — interview secured. Prepare for interview
Safety Surveillance Specialist — Novo Nordisk Closed Apr 27 — not applied. Closed
Senior COA Manager — Novo Nordisk Closed Apr 28 — not applied. Closed
Clinical Data Manager — ALK Submitted — CV + short motivation via EasyCruit. Done
Data Scientist / Biostatistician — UKE Hamburg Closed May 11 — not applied. Closed
Analyst/Senior Analyst RWE — Signum Life Science Applied; phone call then no response — closed. Closed
Statistician / Senior Statistician — Copenhagen Research Centre for Biological & Precision Psychiatry Closed May 17 — not applied. Closed
Scientist, Computational Audiology & ML — Demant / Eriksholm Closed May 17 — not applied. Closed
Senior CDM Professional — Novo Nordisk Not applied — prioritised ALK Clinical Data Manager instead. Skipped
AI & Digital Transformation Professional — Lundbeck Closed May 15 — not applied. Closed
4 Safety Surveillance Principal Specialist — Novo Nordisk Strong fit (8.5/10). Deadline Jun 14 — 13 days. Closest role to the April Specialist (9.2/10) you nearly applied to. Clinical development safety focus; pharma-level seniority expected. CV + cover letter via Novo Nordisk careers portal by Jun 13
5 Postdoc — Precision Cardiometabolic Medicine — Novo Nordisk Good fit (8.0/10). Deadline Jun 15 — 14 days. Fixed-term 1.5-year postdoc — assess against career goals. Computational ML on multi-cohort cardiometabolic data: direct match to your profile. CV + cover letter via Novo Nordisk careers portal by Jun 14
5b Postdoctoral Researcher — Health Economics & Data Science — Novo Nordisk Fit 7.0/10. Deadline Jun 15 — 6 days. RWD and data science profile maps well; health economic modelling (HTA, cost-effectiveness) is the key gap. Weakest fit among the three Jun 15 NN roles — apply if bandwidth allows after the other two. CV + cover letter via Novo Nordisk careers portal by Jun 14
Health Data Scientist — Medical Evidence (Nordics) — AstraZeneca Submitted — CV + cover letter via AstraZeneca careers portal. Deadline was Jun 18. Done
6 Epidemiology Manager (maternity cover) — Novo Nordisk Strongest fit in the batch (9.5/10). Deadline Jun 28, but applications reviewed continuously — apply now. PASS / RWE / pharmacoepidemiology / Danish registries: your exact PhD profile. 12-month FTC with permanent conversion potential. CV + cover letter via Novo Nordisk careers portal — apply immediately
7 Data Analyst — Hempel Lowest fit (5.5/10). Marine domain mismatch; BI/Power BI focus differs from your analytical stack. Apply only if other options thin out. CV + motivation letter via Workday portal