This role is for one of Weekday’s clients
Salary range: Rs 3000000 - Rs 4000000 (ie INR 30 - 40 LPA)

Min Experience: 7+ years
Location: Pune, Maharashtra, India
JobType: full-time

We are seeking an experienced and technically strong Lead Data Scientist to lead client-facing analytics engagements and deliver advanced AI/ML solutions for global pharmaceutical and life sciences organisations. The role combines hands-on data science expertise, pharmaceutical commercial and marketing analytics, client engagement, and team leadership.

The ideal candidate will have strong proficiency in Python, SQL, machine learning, statistical modelling, forecasting, and cloud technologies, along with practical experience working with pharmaceutical datasets. They will lead analytics projects end to end, translate complex business challenges into scalable data-driven solutions, and present actionable recommendations to senior stakeholders.

This role requires a balance of technical depth, commercial understanding, stakeholder management, and the ability to mentor analytics teams while ensuring high-quality project delivery.

Requirements

Key Responsibilities

1. Data Science and Analytics Solution Delivery

• Partner with clients and business stakeholders to understand business challenges, define analytical problems, and develop appropriate data science solutions.
• Lead end-to-end analytics engagements, from problem definition and data exploration to model development, deployment, and impact measurement.
• Design and implement scalable advanced analytics, machine learning, and AI solutions for pharmaceutical business requirements.
• Develop predictive models, forecasting solutions, optimisation frameworks, and analytical algorithms to support commercial and marketing decision-making.
• Apply statistical modelling, hypothesis testing, feature engineering, segmentation, and model validation techniques to generate meaningful insights.
• Translate analytical findings into actionable recommendations through clear, data-driven storytelling and presentations.

2. Pharmaceutical Commercial Analytics

• Deliver analytics solutions across pharmaceutical sales, commercial performance, brand performance, market intelligence, and forecasting.
• Analyse prescription, claims, patient-level commercial, and other relevant pharmaceutical datasets to identify trends and business opportunities.
• Develop solutions for sales force effectiveness, territory alignment, customer targeting, and segmentation.
• Support commercial performance measurement, incentive compensation analytics, product launch analytics, and market share analysis.
• Use pharmaceutical data sources such as IQVIA, Symphony Health, MMIT, Truven, and HealthJump, where relevant.
• Collaborate with commercial, sales, market access, brand, and strategy teams to address business priorities through analytics.

3. Marketing Analytics and AI/ML

• Design and deliver pharmaceutical marketing analytics solutions to measure campaign effectiveness and optimise marketing investments.
• Develop and implement Marketing Mix Modelling (MMM/MMX), Multi-Touch Attribution (MTA), omnichannel analytics, and customer journey analytics solutions.
• Build customer segmentation, physician analytics, recommendation engines, and Next Best Action (NBA) models.
• Apply machine learning, predictive analytics, time-series forecasting, and natural language processing to solve commercial and marketing challenges.
• Explore and implement Generative AI and large language model (LLM)-based applications for relevant pharmaceutical analytics use cases.
• Identify opportunities to improve customer engagement, campaign performance, and commercial outcomes through data-driven strategies.

4. Technical Leadership and Best Practices

• Lead, mentor, and develop a team of data scientists and analysts, fostering technical excellence and continuous learning.
• Conduct technical solution reviews, code reviews, and mentoring sessions to maintain high standards of analytical quality.
• Establish and promote best practices across data science, machine learning, AI, cloud technologies, and analytics delivery.
• Guide the selection of appropriate modelling approaches, validation methods, and deployment strategies based on business requirements.
• Ensure analytical solutions are scalable, reliable, maintainable, and aligned with project objectives.
• Support the adoption of MLOps practices and cloud-based machine learning deployments where applicable.

5. Client Engagement and Stakeholder Management

• Build strong relationships with senior client stakeholders and serve as a trusted analytics advisor.
• Lead client discussions to clarify requirements, present analytical findings, and recommend data-driven business strategies.
• Collaborate with pharmaceutical commercial, marketing, brand, sales, market access, and strategy teams.
• Contribute to solution design, capability development, proposal preparation, and client presentations.
• Communicate complex technical concepts and analytical outcomes effectively to both technical and non-technical audiences.

6. Project and Delivery Management

• Oversee multiple analytics engagements and ensure timely, high-quality delivery against agreed objectives.
• Manage project priorities, timelines, dependencies, and resource requirements.
• Identify delivery risks and implement appropriate mitigation strategies.
• Measure solution effectiveness and business impact to ensure analytics initiatives deliver tangible value.
• Encourage continuous improvement in project execution, technical processes, and client delivery.

Must-Have Skills and Qualifications

• Experience: Strong professional experience in data science, advanced analytics, and machine learning, including relevant leadership and client-facing responsibilities.
• Python: Advanced proficiency in Python for data analysis, statistical modelling, machine learning, and solution development.
• SQL: Advanced proficiency in SQL for querying, transforming, and analysing large datasets.
• Machine Learning: Strong expertise in predictive modelling, statistical methods, hypothesis testing, feature engineering, model validation, and forecasting.
• Pharmaceutical Analytics: Hands-on experience in pharmaceutical commercial analytics, sales analytics, market analytics, brand analytics, performance analytics, or forecasting.
• Pharmaceutical Marketing Analytics: Experience in marketing mix modelling, multi-touch attribution, campaign effectiveness, customer segmentation, omnichannel analytics, or related areas.
• Pharmaceutical Data: Working knowledge of relevant datasets, such as prescription data, claims data, patient-level commercial data, and pharmaceutical market intelligence sources.
• Data Visualisation: Experience with Power BI or Tableau for analytical reporting and business insights.
• Cloud Technologies: Exposure to at least one cloud platform, such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Leadership: Demonstrated ability to mentor analytics teams, review technical solutions, and drive high-quality project delivery.
• Client Management: Strong stakeholder management, problem-solving, presentation, and communication skills.

Good-to-Have Skills

• Experience with Generative AI, Agentic AI, NLP, and LLM-based analytics applications.
• Knowledge of time-series forecasting, recommendation engines, physician analytics, and Next Best Action solutions.
• Familiarity with MLOps and production deployment of machine learning models.
• Experience with Databricks and Snowflake.
• Background in healthcare or pharmaceutical consulting.
• Experience working with global pharmaceutical clients.
• Exposure to advanced cloud-based machine learning deployments and scalable analytics platforms.

Educational Qualifications

A bachelor's or master's degree in Statistics, Mathematics, Computer Science, Engineering, Data Science, Analytics, or a related quantitative discipline is preferred. An MBA or equivalent business qualification is an added advantage.