Job Posting
Associate Director, Clinical Data Scientist - Statistics
Warsaw, Mazovia Job ID R0189902 Category Data Sciences Subcategory Research & Development Business Unit Research & Development Job Type Full timeBy clicking the “Apply” button, I understand that my employment application process with Takeda will commence and that the information I provide in my application will be processed in line with Takeda’s Privacy Notice and Terms of Use. I further attest that all information I submit in my employment application is true to the best of my knowledge.
Job Description
About the role:
At Takeda, we are a forward-looking, world-class R&D organization that unlocks innovation and delivers transformative therapies to patients. By focusing R&D efforts on four therapeutic areas and other targeted investments, we push the boundaries of what is possible in order to bring life-changing therapies to patients worldwide.
You will join Data and Quantitative Sciences (DQS) as an Associate Director-level clinical data science leader, helping turn clinical, biomarker, and external data into clear evidence that supports clinical development decisions. You will lead fit-for-purpose statistical, data science, and advanced analytics work across studies, assets, or specialty areas, while partnering with Clinical, Clinical Pharmacology, PSPV, Clinical Data Management, Translational Sciences, Regulatory, Clinical Operations, and external partners. You will also help advance modern ways of working through artificial intelligence, machine learning, automation, reusable analytics workflows, and strong data standards, while keeping scientific rigor, regulatory expectations, and patient needs at the center.
How you will contribute:
- As Associate Director, Clinical Data Scientist - Statistics, you will design and execute quantitative analyses using clinical trial data, biomarkers, real-world data, external data, and other relevant sources to generate clear insights for study teams and decision-making forums.
- Apply statistical, machine learning, simulation, and visualization methods to support patient-level prediction, endpoint interpretation, risk assessment, scenario planning, and evidence generation.
- Carry out end-to-end data analysis, including hypothesis development, experimental design, analysis planning, data cleaning, analysis execution, and preparation of reports and documentation.
- Provide or help secure internal and external statistical expertise and capacity to support development work.
- Lead clinical data science strategy and delivery for one or more studies, assets, or capability areas, aligning work with development goals, timelines, quality expectations, and partner needs.
- Provide scientific and technical oversight of internal and external delivery partners, including review of analysis plans, specifications, code, outputs, data visualization, and interpretation of findings.
- Identify, communicate, and reduce risks related to data quality, analytic assumptions, vendor delivery, timelines, reproducibility, and regulatory acceptability of data science outputs.
- Assess and communicate internal, external, resource, and quality issues that could affect deliverables or timelines at the program level, and propose practical solutions.
- Partner with Clinical Pharmacology, PSPV, Translational Sciences, Clinical Data Management, Regulatory, and platform teams to make sure CDISC, submission, and downstream quantitative decision-making needs are built into study setup, data review, and reporting processes.
- Define requirements for model-ready datasets and analytics-ready data flows, including variable derivations, data quality expectations, lineage, traceability, metadata, and documentation for regulated clinical development use.
- Mentor junior colleagues and delivery partners in clinical data science methods, reproducible analytic practices, technical problem solving, and clear communication of quantitative insights.
- Increase external recognition of Takeda’s data science work by contributing to conferences, publishing work, and developing external collaborations.
- Drive continuous improvement in clinical data science practices through reusable code, standards, training, mentoring, automation, artificial intelligence-enabled workflow improvements, and adoption of industry best practices.
Minimum Requirements/Qualifications:
- PhD in statistics, biostatistics, data science, applied mathematics, physics, epidemiology, biomedical engineering, computer science, quantitative sciences, or a related field with 5 or more years of relevant experience; or Master of Science with 8 or more years of relevant experience.
- Significant experience in clinical development within the pharmaceutical, biotechnology, or healthcare research environment, with demonstrated ability to influence cross-functional decisions at study, asset, or functional level.
- Experience providing technical leadership, matrix leadership, vendor oversight, and mentoring junior colleagues or delivery partners.
- Advanced knowledge of clinical trial design, drug development, endpoints, estimands, biomarkers, data interpretation, and the role of analytics in clinical decision making.
- Strong foundation in statistics and quantitative methods, including longitudinal analysis, survival methods, causal reasoning, simulation, predictive modeling, and uncertainty communication.
- Experience integrating and interpreting diverse data sources, including clinical trial, biomarker, real-world, external, imaging, digital health, or other high-dimensional data as appropriate to the portfolio.
Practical understanding of artificial intelligence and machine learning and advanced analytics in regulated clinical development, including model development, validation, documentation, bias and assumption assessment, and fit-for-purpose deployment. - Hands-on proficiency in SAS, with working knowledge of R or Python and Structured Query Language; ability to review and guide reproducible analyses, code quality, version control, and validated workflows.
- Ability to work independently on complex datasets, including data cleaning, algorithm development, statistical analysis, and documentation.
- Working knowledge of Clinical Data Interchange Standards Consortium standards, including Study Data Tabulation Model, Analysis Data Model, controlled terminology, Define-XML concepts, and submission-oriented data expectations.
- Knowledge of Food and Drug Administration, European Medicines Agency, International Council for Harmonisation Good Clinical Practice, good practice expectations, data privacy, inspection readiness, and traceability relevant to clinical data and quantitative deliverables.
- Working knowledge of UNIX operating systems is preferred, ideally with experience in high-performance computing environments.
- Communicates complex quantitative findings clearly to scientific, operational, technical, and senior leadership audiences.
- Influences across functions without relying on direct authority; builds trusted partnerships with clinical, statistical, programming, data management, regulatory, technology, and vendor colleagues.
- Balances scientific rigor, speed, quality, and practical delivery; proactively escalates risks with options and recommendations.
- Demonstrates enterprise mindset, curiosity, continuous improvement, and commitment to developing others and advancing modern clinical data science capabilities.
About the role:
At Takeda, we are a forward-looking, world-class R&D organization that unlocks innovation and delivers transformative therapies to patients. By focusing R&D efforts on four therapeutic areas and other targeted investments, we push the boundaries of what is possible in order to bring life-changing therapies to patients worldwide.
As an Associate Director-level clinical data science leader within Data & Quantitative Sciences, you will translate complex clinical, biomarker, and external data into actionable evidence that informs clinical development decisions.
How you will contribute:
- Lead fit-for-purpose statistical, data science, and advanced analytics approaches across assigned studies, assets, or specialty areas, including exploratory analysis, predictive modeling, simulation, and integrated data review.
- Partner cross-functionally with Clinical, Clinical Pharmacology, PSPV, Clinical Data Management, Translational Sciences, Regulatory, Clinical Operations, and external partners to ensure high-quality, traceable, analysis and submission-ready data and decision-ready insights.
- Advance modern ways of working by applying AI/ML, automation, reusable analytics workflows, and governed data standards while maintaining scientific rigor, regulatory awareness, and patient-focused decision making.
- Lead clinical data science strategy and delivery for one or more studies, assets, or capability areas, ensuring alignment with development objectives, timelines, quality expectations, and stakeholder needs.
- Design and/or execute quantitative analyses using clinical trial data, biomarkers, real-world data, external data, and other relevant sources to generate interpretable insights for study teams and governance forums.
- Apply appropriate statistical, machine learning, simulation, and visualization methods to support patient-level prediction, endpoint interpretation, risk assessment, scenario planning, and evidence generation.
- Define requirements for model-ready datasets and analytics-ready data flows, including variable derivations, data quality expectations, lineage, traceability, metadata, and documentation sufficient for regulated clinical development use.
- Partner with Clinical Pharmacology PSPV, Translational Sciences, Clinical Data Management, Regulatory, and platform teams to ensure that CDISC, submission, and downstream quantitative decision-making needs are built into study setup, data review, and reporting processes.
- Provide scientific and technical oversight of internal and external delivery partners, including review of analysis plans, specifications, code, outputs, data visualization, and interpretation of findings.
- Identify, communicate, and mitigate risks related to data quality, analytic assumptions, vendor delivery, timelines, reproducibility, and regulatory acceptability of data science outputs.
- Drive continuous improvement in clinical data science practices through reusable code, standards, training, mentoring, automation, AI-enabled workflow improvements, and adoption of industry best practices.
- Mentor junior colleagues or delivery partners in clinical data science methods, reproducible analytic practices, technical problem solving, and effective communication of quantitative insights.
Minimum Requirements/Qualifications:
- PhD in statistics, biostatistics, data science, epidemiology, biomedical engineering, computer science, quantitative sciences, or related field with 5+ years of relevant experience; or MS with 8+ years of relevant experience. Equivalent combinations should be reviewed with HR.
- Significant experience in clinical development within the pharmaceutical, biotechnology, or healthcare research environment, with demonstrated ability to influence cross-functional decisions at study, asset, or functional level.
- Experience providing technical leadership, matrix leadership, vendor oversight, and/or mentorship of junior colleagues or delivery partners.
- Advanced knowledge of clinical trial design, drug development, endpoints, estimands, biomarkers, data interpretation, and the role of analytics in clinical decision making.
- Strong foundation in statistics and quantitative methods, including longitudinal analysis, survival methods, causal reasoning, simulation, predictive modeling, and uncertainty communication.
- Hands-on proficiency in R and/or Python, with working knowledge of SAS and SQL; ability to review and guide reproducible analyses, code quality, version control, and validated workflows.
- Working knowledge of CDISC standards, including SDTM, ADaM, controlled terminology, Define-XML concepts, and submission-oriented data expectations.
- Experience integrating and interpreting diverse data sources, including clinical trial, biomarker, real-world, external, imaging, digital health, or other high-dimensional data as appropriate to the portfolio.
- Practical understanding of AI/ML and advanced analytics in regulated clinical development, including model development, validation, documentation, bias/assumption assessment, and fit-for-purpose deployment.
- Knowledge of FDA, EMA, ICH-GCP, GxP, data privacy, inspection readiness, and traceability expectations relevant to clinical data and quantitative deliverables.
- Communicates complex quantitative findings clearly to scientific, operational, technical, and senior leadership audiences.
- Influences across functions without relying on direct authority; builds trusted partnerships with clinical, statistical, programming, data management, regulatory, technology, and vendor stakeholders.
- Balances scientific rigor, speed, quality, and pragmatic delivery; proactively escalates risks with options and recommendations.
- Demonstrates enterprise mindset, curiosity, continuous improvement, and commitment to developing others and advancing modern clinical data science capabilities.
More about us:
At Takeda, we are transforming patient care through the development of novel specialty pharmaceuticals and best in class patient support programs. Takeda is a patient-focused company that will inspire and empower you to grow through life-changing work.
Certified as a Global Top Employer, Takeda offers stimulating careers, encourages innovation, and strives for excellence in everything we do. We foster an inclusive, collaborative workplace, in which our teams are united by an unwavering commitment to deliver Better Health and a Brighter Future to people around the world.
This position is currently classified as "hybrid" following Takeda's Hybrid and Remote Work policy.
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