Engagement Lead - Data Science

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

The Position

Engagement Lead - Data Scientist

Roche India - Roche Services & Solutions

Hyderabad

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love.

That’s what makes us Roche.

Roche has established the Global Analytics and Technology Center of Excellence (GATE) as part of Roche Services & Solutions (RSS) India to drive analytics and technology-driven solutions by partnering with Roche affiliates across the globe. RSS is a global division operating in an agile organizational setup without extensive hierarchical structure, where all our employees lead from every seat, and inspirational leadership, and an entrepreneurial customer-centric mindset are our core values.

Your Opportunity

AsEngagement Lead, you will lead two data science sub-streams supporting commercial teams: Predictive AI & Optimization and Causal AI & Experimentation. You will own the technical direction and delivery of both. You will work directly with product owners to turn business questions into predictive, causal and optimization solutions, and you will grow a team of about 7 data scientists.

This is a hands-on leadership role. You will review models and code, unblock the team quickly, and be accountable for the quality and business impact of everything the two sub-streams deliver.

Key Responsibilities:

• Lead, mentor and develop Senior Data Scientists and Data Scientists across both sub-streams. Build technical depth, modular coding standards and agile delivery habits
• Lead the design, calibration and validation of Bayesian and frequentist Marketing Mix Models (MMM). Models should account for adstock, saturation, seasonality and hierarchical or geo-level effects
• Oversee multi-touch attribution (MTA) and brand performance analytics. Translate the results into practical recommendations on channel mix and spend
• Design and govern experiments, including A/B tests, quasi-experimental designs (e.g., difference-in-differences, synthetic control) and econometric analysis, to measure the true impact of commercial initiatives
• Oversee the design and delivery of predictive models for commercial use cases. This includes classification on highly imbalanced data, anomaly detection, segmentation and time-series forecasting
• Turn predictions into decisions through optimization: budget and resource allocation, response curves, ROI/mROI analysis and scenario-planning simulations
• Rapid Prototyping & Business Value: Quickly design and build proofs of concept, prototypes and business case studies, including GenAI- and agent-based solutions where they add value, to show tangible impact
• Set the methodological standards for each sub-stream. Decide which approaches are suitable for a given problem, and explain why others are not
• Stay close to the work: review model designs, validation results and critical code, and resolve technical blockers quickly
• Present findings and trade-offs clearly to both technical and non-technical audiences, so that complex model results lead to confident decisions
• Work with global cross-functional teams in an agile environment, including demos, reviews and retrospectives to keep improving deliverables
• Ensure reproducibility, documentation and peer review across all models delivered

Who You Are

You are an accomplished data science leader with deep expertise in both predictive modelling and causal inference. You know when a problem needs a prediction, when it needs a causal estimate, and when it needs an optimization layer on top. You lead by example, stay close to the code, and can explain complex model outputs, from an MMM posterior to a SHAP plot, in clear business terms that product owners can act on.

Education & Certification:

• Educational Background: Bachelor's or Master's degree in Data Science, Statistics, Economics, Artificial Intelligence and Machine Learning, Computer Science, Business Analytics or another quantitative field.
• Certifications: Recognized professional certifications in Data Science, Machine Learning, Cloud AI Platforms or Advanced Analytics are an advantage.

Qualifications & Technical Skillset:

• 10+ years of experience in Data Science/Analytics, including at least 3 years leading data science teams and mentoring data scientists. You should have a proven track record of measurable business impact through predictive modelling and causal inference or experimentation, ideally in commercial or marketing analytics.
• Strong hands-on expertise in predictive modelling, including classification on skewed data with imbalance handling (e.g., SMOTE, class weighting), ensemble and tree-based models (LightGBM, XGBoost, CatBoost), linear models, anomaly detection, unsupervised methods (clustering, segmentation) and time-series forecasting (e.g., ARIMA, Prophet).
• Rigorous model validation and explainability, including SHAP and precision-recall and calibration methods for imbalanced problems.
• Deep expertise in causal inference and experimentation, including experimental design, A/B testing, quasi-experimental methods and econometrics.
• Hands-on experience building Marketing Mix Models and multi-touch attribution models (regression-based, time-decay, Markov-chain and Shapley-value methods), using frameworks such as Google Meridian, PyMC-Marketing, Meta Robyn, LightweightMMM, Orbit, PyMC or Stan. This includes model diagnostics (convergence, priors, posterior predictive checks) and turning outputs into budget optimization, response curves and scenario simulations.
• Expert proficiency in Python and SQL, with strong experience in Spark (distributed computing), Airflow, Git, AWS, and Tableau or an equivalent tool for visualization and storytelling.
• Exposure to and recent experience with GenAI tools and LLMs (e.g., OpenAI, Claude, Gemini, DeepSeek) and coding assistants such as GitHub Copilot, plus experience of building AI agents using frameworks such as LangChain, LangGraph, AutoGen or CrewAI.

Note: This job description is intended as a general guideline for the responsibilities and qualifications required for this position. It is not an exhaustive list, and responsibilities may evolve and change based on business needs.

Who we are

A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.

Let’s build a healthier future, together.

Roche is an Equal Opportunity Employer.