Senior Data Scientist

Grade: C3

Shift:

2 pm to 11 pm IST

Role:

Individual contributor role.

Location:

Manyata Tech Park, Bangalore.

Introduction to role:

Are you ready to dive into the world of commercial analytics and make a real impact? As a Data Scientist, you'll partner with cross-functional teams to tackle business challenges head-on using data-driven solutions. Your mission will be to build, develop, and deploy advanced AI and machine learning models. You will use diverse healthcare datasets to uncover actionable insights and optimize business processes.

Accountabilities:

• Serve as a subject matter expert in statistical methodologies including hypothesis testing, confidence intervals, power analysis, ANOVA, regression modelling, and significance testing.
• Design and interpret advanced regression models including linear, logistic, mixed-effects, Poisson, and regularised approaches.
• Apply Bayesian and frequentist statistical frameworks to ensure robust analytical outcomes.
• Evaluate statistical significance using multiple comparison correction methodologies and appropriate inference techniques.
• Develop, validate, and deploy predictive models using machine learning techniques including XGBoost, LightGBM, Random Forest, SVM, neural networks, and ensemble methods.
• Build survival and time-to-event models to support patient adherence, discontinuation risk, and patient lifetime value analyses.
• Develop forecasting solutions for demand planning, revenue projections, and business scenario modelling.
• Design experimental and quasi-experimental studies to measure the causal impact of commercial interventions.
• Build and optimise Marketing Mix Models to measure channel effectiveness, promotional ROI, and commercial investment performance.
• Develop Next-Best-Action recommendation engines to support field force and customer engagement strategies.
• Design patient identification and targeting models leveraging healthcare datasets including claims, laboratory, and specialty pharmacy data.
• Develop adherence, retention, segmentation, targeting, and competitive intelligence solutions to drive business performance.
• Leverage Snowflake Cortex AI and AI coding agents to accelerate model development, feature engineering, and analytical workflows.
• Validate and assess AI-generated outputs for statistical accuracy, business relevance, and analytical integrity.
• Design benchmarking and evaluation frameworks to continuously improve AI-assisted analytical capabilities.
• Contribute to developing AI-first solutions that enhance commercial decision-making and operational efficiency.
• Ensure all analytical activities adhere to HIPAA regulations and organisational governance standards.
• Operate exclusively on de-identified patient-level data while maintaining data privacy, access controls, and auditability.
• Implement explainability, fairness, drift monitoring, and bias detection frameworks across predictive models.
• Support compliance with healthcare and pharmaceutical regulations including GDPR, FDA, REMS, SOC2, and enterprise AI governance requirements.
• Partner with Commercial, Market Access, Patient Services, Brand, and Field teams to translate business challenges into analytical solutions.
• Present complex analytical findings to technical and non-technical stakeholders, including senior leadership.
• Maintain clear documentation of methodologies, assumptions, validation approaches, and model limitations.
• Support collaboration, code reviews, and effective methods adoption across the analytics organisation.

Essential Skills/Experience:

• Master's or PhD in Statistics, Biostatistics, Data Science, Econometrics, Applied Mathematics, or a related quantitative discipline, with 4-8+ years of relevant experience in healthcare, life sciences, or commercial analytics.
• Strong focus on AI-first problem solving. Experience applying Generative AI, AI coding agents, and intelligent systems to speed up analytics delivery while preserving analytical rigour.
• Advanced proficiency in hypothesis testing, regression analysis, ANOVA, survival analysis, Bayesian inference, experimental design, power analysis, significance testing, and small-sample statistical methodologies.
• Hands-on experience developing, deploying, and validating predictive models using XGBoost, LightGBM, Random Forest, SVM, neural networks, ensemble methods, and time-series forecasting algorithms.
• Expert-level proficiency in Python and SQL with experience using machine learning, statistical modelling, and explainability libraries. Familiarity with Git, Jupyter, and CI/CD workflows.
• Experience working with Snowflake, Snowpark, Spark/PySpark, MLflow, cloud-based data platforms, and scalable analytical environments.
• Experience working with de-identified patient-level data including claims, specialty pharmacy, laboratory, EMR/EHR, and epidemiology datasets.
• Knowledge of customer segmentation, targeting, patient identification, adherence analytics, forecasting, recommendation systems, and commercial effectiveness measurement.
• Working knowledge of HIPAA requirements, explainable AI frameworks (SHAP, LIME), model monitoring, bias detection, and governance practices in regulated environments.
• Ability to communicate complex statistical and analytical concepts clearly to business stakeholders and senior leaders while working effectively in cross-functional global teams.

Desirable Skills/Experience:

• Experience working with Agentic AI frameworks, autonomous decision systems, prompt engineering, AI evaluation frameworks, and AI governance.
• Hands-on experience developing Bayesian Media Mix Models using tools such as PyMC, LightweightMMM, Robyn, or similar frameworks.
• Experience designing recommendation systems using reinforcement learning, multi-armed bandits, collaborative filtering, or related approaches.
• Experience supporting rare disease or specialty pharmaceutical businesses, including patient identification, specialty pharmacy data, and high-value therapy environments.
• Experience working with IQVIA, Komodo Health, Veeva CRM, MMIT, Model N, specialty pharmacy data, laboratory datasets, and EMR/EHR sources.
• Expertise applying causal inference methodologies including propensity matching, difference-in-differences, synthetic controls, instrumental variables, and regression discontinuity techniques.
• Experience with NLP, topic modelling, NER, embeddings, transformers, and deep learning techniques for healthcare analytics applications.
• Experience building executive dashboards and self-service reporting solutions using Power BI, Tableau, or Qlik.
• Experience operationalising machine learning pipelines, model registries, monitoring frameworks, and experiment tracking tools.
• Familiarity with Veeva CRM, commercial operations systems, field force effectiveness platforms, and commercial analytics ecosystems.