Grade : C3
Shift: 2 pm to 11 pm ISTRole: 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.
Education & 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.
AI-Native Data Science Orientation: 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..
Statistical Expertise: Advanced proficiency in hypothesis testing, regression analysis, ANOVA, survival analysis, Bayesian inference, experimental design, power analysis, significance testing, and small-sample statistical methodologies.
Predictive Analytics & Machine Learning: Hands-on experience developing, deploying, and validating predictive models using XGBoost, LightGBM, Random Forest, SVM, neural networks, ensemble methods, and time-series forecasting algorithms.
Programming & Analytics Tools: 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.
Data Platforms & Engineering: Experience working with Snowflake, Snowpark, Spark/PySpark, MLflow, cloud-based data platforms, and scalable analytical environments.
Healthcare Data Expertise: Experience working with de-identified patient-level data including claims, specialty pharmacy, laboratory, EMR/EHR, and epidemiology datasets.
Commercial Analytics Experience: Knowledge of customer segmentation, targeting, patient identification, adherence analytics, forecasting, recommendation systems, and commercial effectiveness measurement.
Compliance & Governance: Working knowledge of HIPAA requirements, explainable AI frameworks (SHAP, LIME), model monitoring, bias detection, and governance practices in regulated environments.
Communication & Collaboration: 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:
Agentic AI & Advanced AI Systems: Experience working with Agentic AI frameworks, autonomous decision systems, prompt engineering, AI evaluation frameworks, and AI governance.
Marketing Mix Modelling: Hands-on experience developing Bayesian Media Mix Models using tools such as PyMC, LightweightMMM, Robyn, or similar frameworks.
Next-Best-Action Analytics: Experience designing recommendation systems using reinforcement learning, multi-armed bandits, collaborative filtering, or related approaches.
Rare Disease & Specialty Pharma Analytics: Experience supporting rare disease or specialty pharmaceutical businesses, including patient identification, specialty pharmacy data, and high-value therapy environments.
Healthcare Industry Data Sources: Experience working with IQVIA, Komodo Health, Veeva CRM, MMIT, Model N, specialty pharmacy data, laboratory datasets, and EMR/EHR sources.
Causal Inference & Advanced Analytics: Expertise applying causal inference methodologies including propensity matching, difference-in-differences, synthetic controls, instrumental variables, and regression discontinuity techniques.
Deep Learning & NLP: Experience with NLP, topic modelling, NER, embeddings, transformers, and deep learning techniques for healthcare analytics applications.
Visualisation & BI Tools: Experience building executive dashboards and self-service reporting solutions using Power BI, Tableau, or Qlik.
Model Operations & AI Enablement: Experience operationalising machine learning pipelines, model registries, monitoring frameworks, and experiment tracking tools.
Pharmaceutical Commercial Systems: Familiarity with Veeva CRM, commercial operations systems, field force effectiveness platforms, and commercial analytics ecosystems.
When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.
At AstraZeneca's Alexion division for Rare Diseases, you'll find an environment where innovation thrives! Our commitment to patients drives us forward every day. Here you'll be part of a team that values diversity of thought as much as diversity of people. We believe in empowering our employees through tailored development programs that align personal growth with our mission. With a rapidly expanding portfolio in a dynamic biotech atmosphere combined with the resources of a global biopharma leader—your career here is more than just a path—it's a journey towards making a difference where it truly counts.
Ready to make an impact? Apply now!
Date Posted
26-Aug-2026Closing Date
04-Sept-2026Alexion is proud to be an Equal Employment Opportunity and Affirmative Action employer. We are committed to fostering a culture of belonging where every single person can belong because of their uniqueness. The Company will not make decisions about employment, training, compensation, promotion, and other terms and conditions of employment based on race, color, religion, creed or lack thereof, sex, sexual orientation, age, ancestry, national origin, ethnicity, citizenship status, marital status, pregnancy, (including childbirth, breastfeeding, or related medical conditions), parental status (including adoption or surrogacy), military status, protected veteran status, disability, medical condition, gender identity or expression, genetic information, mental illness or other characteristics protected by law. Alexion provides reasonable accommodations to meet the needs of candidates and employees. To begin an interactive dialogue with Alexion regarding an accommodation, please contact [email protected]. Alexion participates in E-Verify.
Skills Required
- Master’s or PhD in Statistics, Biostatistics, Data Science, Econometrics, Applied Mathematics, or a related quantitative discipline
- 4–8+ years of relevant experience in healthcare, life sciences, or commercial analytics
- Experience applying Generative AI, AI coding agents, and intelligent systems to analytics workflows
- Advanced expertise in hypothesis testing, regression analysis, ANOVA, survival analysis, Bayesian inference, experimental design, power analysis, significance testing, and small-sample methodologies
- Hands-on experience developing, deploying, and validating predictive models using XGBoost, LightGBM, Random Forest, SVM, neural networks, ensemble methods, and time-series forecasting
- Expert-level proficiency in Python and SQL
- Experience with machine learning, statistical modeling, and explainability libraries
- Familiarity with Git, Jupyter, and CI/CD workflows
- Experience with Snowflake, Snowpark, Spark or PySpark, MLflow, cloud data platforms, and scalable analytics environments
- Experience working with de-identified patient-level healthcare 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, explainable AI, model monitoring, bias detection, and governance in regulated environments
- Ability to communicate complex statistical and analytical concepts to business stakeholders and senior leaders
- Experience with Agentic AI frameworks, autonomous decision systems, prompt engineering, AI evaluation frameworks, and AI governance
- Experience developing Bayesian Marketing Mix Models using PyMC, LightweightMMM, Robyn, or similar tools
- Experience designing Next-Best-Action recommendation systems using reinforcement learning, multi-armed bandits, collaborative filtering, or related approaches
- Experience supporting rare disease or specialty pharmaceutical analytics
- Experience with IQVIA, Komodo Health, Veeva CRM, MMIT, Model N, specialty pharmacy, laboratory, or EMR/EHR data
- Expertise in causal inference methods such as propensity matching, difference-in-differences, synthetic controls, instrumental variables, and regression discontinuity
- Experience with NLP, topic modeling, named entity recognition, embeddings, transformers, and deep learning
- Experience building dashboards and self-service reporting with Power BI, Tableau, or Qlik
- Experience operationalizing machine learning pipelines, model registries, monitoring frameworks, and experiment tracking
- Familiarity with Veeva CRM, commercial operations systems, field force effectiveness platforms, and commercial analytics ecosystems
What We Do
AstraZeneca is a global, science-led, patient-focused biopharmaceutical company committed to excellence in the research, development, and commercialization of prescription medicines. With approximately 90,000 employees across 85 countries, the company aims to unlock the power of science to deliver innovative medicines that transform patient outcomes and improve healthcare for people, society, and the planet worldwide.
