Senior Data Scientist

General Description:

The Data Insights (DI) team is seeking a highly motivated Senior Data Scientist to transform trusted enterprise data into AI-enabled decision intelligence that drives faster, smarter, and more effective decisions across Clinical Development and Operations.

Going beyond traditional data science and business intelligence, this role combines advanced analytics, AI, domain expertise, and product thinking to design, develop, and continuously improve scalable decision products, intelligent automation, and AI-powered analytical solutions.

As a hands-on contributor, you will partner with business stakeholders, product managers, data engineers, software engineers, AI scientists, statisticians, and clinical experts to solve complex business challenges, identify high-value opportunities, and translate insights into actionable decisions and measurable outcomes.

Success will be measured not only by analytical excellence, but also by product adoption, business impact, AI-enabled productivity gains, and the ability to turn data into meaningful decisions.

This role offers an opportunity to apply emerging AI technologies to real-world challenges in biotechnology and pharmaceuticals - helping accelerate drug development and ultimately improve patient outcomes.

Major Responsibilities:

Decision Intelligence & Advanced Analytics

• Turn complex business and clinical challenges into actionable insights and AI-enabled decision solutions that improve business performance and support better patient outcomes.

• Design, develop, and enhance analytical products by combining trusted data, advanced analytics, business context, and AI capabilities.

• Apply statistical analysis, predictive modeling, trend analysis, segmentation, and root cause analysis to uncover opportunities, identify risks, and support better decisions.

• Translate analytical findings into clear recommendations and compelling data stories that help stakeholders take action.

AI-Enabled Analytics & Intelligent Automation

• Explore and apply emerging AI technologies, including large language models (LLMs), AI agents, conversational analytics, and workflow automation, to solve real-world business problems.

• Build and experiment with AI-powered analytical experiences, such as conversational BI, automated insights and narratives, intelligent recommendations, and proactive alerts.

• Identify opportunities to simplify repetitive analytical tasks and develop AI-enabled workflows that improve efficiency, scalability, and user experience.

Decision Products & Business Impact

• Contribute to the end-to-end development of decision intelligence products, from understanding user needs and defining requirements to building, launching, and continuously improving solutions.

• Partner with business stakeholders and product managers to identify high-value opportunities and prioritize solutions that deliver meaningful business impact.

• Define and track key performance indicators (KPIs), product adoption, and business outcomes to understand what works and guide continuous improvement.

• Develop reusable analytical tools, frameworks, and capabilities that can be applied across business functions.

AI Quality, Evaluation & Responsible AI

• Evaluate the accuracy, reliability, and business usefulness of AI-enabled analytical solutions using practical testing, evaluation metrics, and user feedback.

• Support the development of AI evaluation approaches, including benchmark datasets, human-in-the-loop (HITL) reviews, and ongoing performance monitoring.

• Apply responsible AI principles and appropriate data quality, privacy, and compliance practices when developing solutions in a regulated environment.

Collaboration, Innovation & Continuous Learning

• Work closely with colleagues across Clinical Development, Statistics, Scientific Programming, Medical, Data Engineering, Product Management, and AI Engineering to solve meaningful business challenges.

• Communicate complex analytical concepts and AI capabilities in clear, practical terms to technical and non-technical audiences.

• Stay curious and experiment with new analytical methods, AI tools, and technologies, bringing promising ideas into practical applications.

• Share knowledge, contribute to technical best practices, and mentor colleagues through collaboration and hands-on problem-solving.

Qualifications:

• Experience: 2+ years of experience in Data Science, Advanced Analytics, Business Intelligence, Product Analytics, Machine Learning, or a related analytical field.

• Technical Skills: Strong proficiency in Python and SQL, with hands-on experience analyzing complex datasets, developing analytical solutions, and building reusable code or tools.

• Analytical Problem-Solving: Strong analytical thinking and problem-structuring skills, with the ability to translate ambiguous business questions into structured analyses, meaningful insights, and actionable recommendations.

• Business Analytics: Experience defining and analyzing KPIs, developing performance metrics, identifying trends, and using data to support business decisions.

• Communication & Collaboration: Ability to communicate complex analytical findings clearly to technical and non-technical audiences and collaborate effectively with cross-functional stakeholders.

• Ownership & Learning Mindset: A proactive, hands-on approach to problem-solving, with curiosity, adaptability, and enthusiasm for learning and applying emerging AI technologies.

Preferred Qualifications — AI, Data Platforms & Domain Experience

• Generative AI & Intelligent Automation: Experience or hands-on exposure to LLMs, prompt engineering, AI agents, retrieval-augmented generation (RAG), conversational AI, or AI-enabled analytical workflows.

• AI-Native Development: Familiarity with AI-assisted development tools such as Claude Code, GitHub Copilot, Codex, or similar tools to accelerate coding, experimentation, and analytical solution development.

• AI-Enabled Analytics: Exposure to Databricks AI/BI Genie or similar conversational analytics and natural-language data exploration platforms.

• AI Product Development: Experience prototyping, developing, or improving AI-enabled analytical applications, decision-support tools, or intelligent automation solutions.

• AI Quality & Evaluation: Familiarity with AI evaluation methods, human-in-the-loop (HITL) workflows, model monitoring, or responsible AI practices.

• Modern Data Platforms: Experience with Databricks, Spark, cloud data platforms, or large-scale data processing and exploratory data analysis.

• Statistical & Development Tools: Experience with R, Posit/RStudio Workbench, Git, collaborative development environments, or automated analytical workflows.

• Life Sciences Domain: Experience in biotechnology, pharmaceuticals, clinical development, healthcare analytics, or other regulated industries is a plus.

Education Required:

Master’s or PhD preferred in Computer Science, Data Science, Statistics, Mathematics, Engineering, Information Systems, Life Sciences, or a related quantitative field, or equivalent practical experience.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.