Senior Data Scientist

Bachelor’s degree in Engineering, Computer Science, Data Science, Statistics, Mathematics, Economics, or a related quantitative discipline. A master’s degree is a plus, but not mandatory. 3–4+ years of relevant experience in data science, advanced analytics, machine learning, forecasting, or a related field.

Strong understanding of machine learning concepts, statistical modeling, and predictive analytics, with hands-on experience developing and validating ML models. Strong experience in time-series forecasting, including forecasting methodologies, model development, validation, performance assessment, and interpretation of results.

Prior experience working in the pharmaceutical/life sciences industry, with a good understanding of pharmaceutical data and analytics. Familiarity with healthcare/pharma data sources such as patient, claims, sales, HCP, market, or other commercial datasets is highly desirable. Ability to translate business questions into structured analytical approaches, data science solutions, and actionable insights.

Strong analytical problem-solving skills with the ability to work through ambiguous or evolving business requirements. Strong communication and stakeholder-management skills, with the ability to serve as an onshore point of contact for clients and offshore delivery teams. Demonstrated willingness to learn new technologies, adapt to changing requirements, and scale across new analytical use cases.

Ability to collaborate effectively with data scientists, engineers, business analysts, consultants, and senior stakeholders. Strong written and verbal communication skills.

Familiarity with Python, SQL, or similar programming and analytics languages Good to Have Exposure to AI agents / agentic AI, including building or working with agents in platforms such as Snowflake Cortex / Snowflake Intelligence or Vertex AI / Vertex AI Agent Builder. Experience working with Dataiku or similar enterprise data science platforms.

Exposure to Generative AI, LLMs, prompt engineering, or AI-enabled analytics solutions.

What You’ll Do

• Develop and implement machine learning and advanced analytics solutions to address complex pharmaceutical and commercial business problems.
• Lead the development of time-series forecasting models, including demand, sales, patient, market, or other business forecasts.
• Analyze and synthesize diverse pharmaceutical and healthcare data sources to generate meaningful insights and support business decisions.
• Translate ambiguous business requirements into analytical frameworks, modeling approaches, and actionable recommendations.
• Build, validate, optimize, and deploy ML and forecasting models, ensuring appropriate performance evaluation and business interpretability.
• Work closely with clients and senior stakeholders to understand business objectives, communicate analytical findings, and translate model outputs into business recommendations.
• Serve as a key onshore point of contact, coordinating with clients and offshore teams to ensure effective communication, prioritization, and delivery.
• Own analytical workstreams end-to-end, from problem definition and data exploration through model development, validation, insights generation, and presentation.
• Collaborate with cross-functional and global teams, including data scientists, data engineers, business analysts, and subject-matter experts.
• Stay current with emerging AI/ML techniques and technologies and identify opportunities to apply them to pharmaceutical analytics and business problems.
• Explore and contribute to AI-agent-based solutions where appropriate, including use cases involving Snowflake or Vertex AI.
• Continuously learn and adapt to new client requirements, technologies, datasets, and analytical challenges.
• Take increasing ownership of analytical workstreams and help scale solutions across multiple pharmaceutical use cases and client engagements.
• Build and present executive-ready outputs, including analytical findings, model results, forecasts, visualizations, and recommendations.
• Core Competencies Machine Learning & Predictive Analytics Time-Series Forecasting Pharmaceutical / Life Sciences Analytics Python / SQL / R Statistical Modeling Data Analysis & Data Interpretation Problem Solving & Structured Thinking Client & Stakeholder Management Communication & Storytelling AI / Generative AI Cross-functional Collaboration Adaptability & Continuous Learning