Senior Data Scientist / Machine Learning (Advanced Analytics – Clinical Development)

Location: Remote

Duration of Contract: Long Term

Role Summary

We are seeking a Senior Data Scientist specializing in Machine Learning to join our Advanced Analytics team supporting clinical research and development. This role focuses on delivering high-impact analytics that influence trial strategy and program decisions. The successful candidate will independently lead end-to-end analytical work—framing ambiguous questions, shaping data into analysis-ready form, applying robust statistical and machine learning methods, and communicating insights clearly to stakeholders.

We are looking for someone with at least 4 years of professional experience as a data scientist/analyst in a pharma or related setting.

Key Responsibilities

• Lead complex clinical analytics: Own analyses across clinical studies and related data sources, tackling problems where the path is not predefined and the work requires strong judgment and rigor.
• Partner with stakeholders: Proactively engage clinical, biometrics, and cross-functional partners to gather requirements, align on analytic intent, refine questions, and deliver decision-ready outputs.
• Build statistical + ML solutions: Select, develop, and validate appropriate statistical approaches and machine learning models to answer clinical development questions; ensure interpretability and defensibility of results.
• Work with pharmacology-adjacent data: Integrate and analyze clinical outcomes with exposure/response or other pharmacology-related datasets where relevant to the question.
• Engineer data at scale: Perform data wrangling, feature engineering, and reproducible pipelines in modern compute environments (Microsoft Fabric or similar); write production-quality analysis code and adhere to team standards.
• Operate within clinical data standards: Work effectively within industry clinical data conventions and structured clinical data models; ensure analysis specifications and outputs align with quality expectations.
• Tell a clear story: Present compelling, validated narratives and visuals that translate complex analytics into insights stakeholders can act on.
• Be a team multiplier: Collaborate effectively, mentor where appropriate, and contribute to a culture of strong teamwork, responsiveness, and continuous improvement.

What Success Looks Like

• You take ownership without waiting for perfect direction, and you bring structure to ambiguity.
• Stakeholders trust you because you communicate early, set expectations, and deliver rigorous, understandable work.
• You can work independently and collaborate smoothly across functions, strengthening partnerships and repeat engagement.

Core Qualifications

• Demonstrated track record delivering analytics in clinical research settings, including working with late-stage clinical datasets and typical clinical development constraints (e.g., endpoint complexity, protocol nuance, data quality realities).
• Strong foundation in statistical reasoning with practical experience applying predictive/ML methods in healthcare/clinical contexts (beyond academic exercises).
• Proficiency in Python or R, with the ability to handle large, complex datasets in distributed or cloud-enabled environments (e.g., Spark-based workflows).
• Comfort working within structured clinical data standards/models and producing analysis outputs that meet quality expectations in regulated environments.
• Strong communication skills: ability to explain methods, assumptions, and results clearly to both technical and non-technical audiences.
• Highly collaborative with strong interpersonal skills; able to build credibility and maintain productive stakeholder relationships over time.

Nice to Have

• Experience supporting programs in one or more of the following therapeutic domains: oncology, dermatology, hematology (or similarly complex disease areas).
• Experience with time-to-event or longitudinal modeling, and/or methods for explaining model outputs to non-technical partners (e.g., interpretable ML, model diagnostics, clear visualization).

Working Style / Leadership Expectations

• Self-starter mindset with strong ownership, organization, and follow-through.
• Ability to lead analytical workstreams, influence without authority, and deliver in a matrixed environment.