Senior Analyst - Brand Analytics

Job Title: Senior Analyst - Brand Analytics Career Level: D1

Introduction to role: Are you ready to turn sophisticated healthcare data into decisions that develop oncology brands and improve patient journeys? How would you design meticulous, test-and-learn experiments that shift market performance and prove return on investment at scale? Based in Bangalore, you will be a hands-on strategic partner to commercial brand teams, delivering high-impact insights that guide choices across Marketing, Sales, Medical, Market Access, and Forecasting. You will translate ambiguous questions into clear analytical approaches, then communicate compelling recommendations that influence senior collaborators. Joining a global network of data and digital specialists, you will work with advanced platforms and rich secondary healthcare data to solve important problems. Your work will accelerate brand performance today and build reusable analytical assets that set the standard for how we operate tomorrow.

ACCOUNTABILITIES: Commercial Analytics and Insight Generation: Partner with assigned brand teams to shape strategy and execution through meticulous, data-driven analysis that informs where to focus and why it matters. Opportunity Identification: Size markets, uncover HCP and patient opportunities, and assess whitespace to prioritize investments that drive measurable impact. Customer Segmentation and Targeting: Build and operationalize segments that sharpen targeting across channels and optimize field and non-personal engagement. Marketing Mix and return on investment: Quantify channel effectiveness, run attribution and econometric analyses, and guide budget reallocation to enhance returns. Test-and-Learn Experimentation: Design and evaluate smart business experiments and pilots; establish success metrics and embed lessons into ongoing plans. Market Simulation and Patient Flow Modelling: Create simulations and patient journey models to pressure-test scenarios and inform trade-off decisions. Advanced Analytics and Data Science: Develop robust, production-ready analytics in Python and SQL (and PySpark where relevant), applying statistical methods, predictive modelling, time series, regression, econometrics, and Bayesian techniques.