Analyst - Data Scientist

Alexion · India - Bangalore · 1 month ago

Category: Data science
Experience: 8+ years
Company size: 1000+

agentic-ai ai ai-enablement analytics causal-inference ci-cd cloud crm data-science deep-learning embeddings

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:

Statistical Analysis & Modelling

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.

Predictive Analytics & Machine Learning

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.

Commercial Analytics & Business Effectiveness

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.

AI-Native Analytics & Agentic AI Enablement

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.

Data Privacy, Compliance & Governance

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.

Collaboration & Stakeholder Management

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:

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.

Analyst - Data Scientist

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