Principal Data Scientist

Overview

The Principal Data Scientist acts as the performance analytics and AI business partner, combining commercial understanding with hands-on data science, machine learning, Generative AI and agentic AI to turn complex performance data into trusted decisions, scalable analytics products and intelligent decision-support tools.

What you'll do

• Act as the day-to-day performance analytics partner for assigned Businesses and Regions, translating leadership questions into analytics, data-science and AI solution requirements.
• Lead analysis across market share, sell-in, sell-out, revenue, margin, price, mix, distribution, customer and channel performance, and support business reviews, planning cycles and forecasts.
• Connect multiple performance signals to identify drivers, risks, opportunities and required actions, and translate complex analysis into concise executive narratives.
• Own the use, interpretation and quality of syndicated market and sell-out data from Circana, GfK, Nielsen, IQVIA and other vendors, including methodologies, coverage, definitions and limitations.
• Work with Data & Technology, data engineering and external providers to integrate, validate and harmonise data in ADL and related platforms.
• Establish reconciliation, validation and quality-control processes, and define common business rules, hierarchies, calculations and performance definitions across Businesses and Regions.
• Own and continuously improve sell-out and commercial performance dashboards as analytics products, building scalable, reusable dashboards and automated workflows using SQL, Python and modern BI tools.
• Embed automated diagnostics, alerts, benchmarks, root-cause analysis and recommended actions into dashboards, and simplify the dashboard landscape by eliminating duplicate and manual reporting.
• Analyse complex structured and unstructured datasets and develop predictive and diagnostic models for forecasting, regression, classification, clustering, anomaly detection, and price and mix analysis.
• Own the end-to-end data-science lifecycle, including problem framing, feature engineering, model development, validation, deployment, monitoring and improvement, and assess model performance, stability, explainability and business relevance.
• Design, build and deploy AI-enabled tools, Generative AI applications and analytical agents that let users ask business questions in natural language and receive trusted, grounded answers.
• Create agents that monitor performance, identify deviations, retrieve context, diagnose likely drivers, simulate scenarios and recommend next-best actions.
• Apply responsible-AI principles, including data security, access control, traceability, explainability, evaluation, bias awareness and clear human decision rights.
• Partner with IT, data engineering, software development and platform teams to ensure analytical and AI solutions are secure, scalable, reliable and aligned with enterprise standards.
• Drive adoption of dashboards, models and AI tools through training, coaching, playbooks and active engagement with business users, and track whether insights lead to decisions and measurable business impact.

What you'll need

• Preferably 9+ years of experience in performance analytics, commercial analytics, data science, decision intelligence, business intelligence or a related field.
• Proven experience with syndicated market or sell-out data, such as Circana, GfK, Nielsen, IQVIA or equivalent.
• Demonstrated experience building and deploying analytics products, dashboards, predictive models or AI-enabled tools, not only consuming reports.
• Strong experience partnering with senior commercial stakeholders and influencing decisions without direct authority.
• Experience working across multiple markets, categories or Business Units in a complex matrix organisation.
• Experience taking analytical solutions from business problem through build, deployment, adoption and measurable impact.
• Expert-level hands-on capability in Python and SQL, with strong foundations in statistics, data modelling and data quality.
• Strong practical expertise in machine learning, including regression, forecasting, classification, clustering, anomaly detection and model evaluation.
• Strong working expertise in Generative AI, LLMs, prompt design, retrieval-augmented generation, AI-agent design and evaluation.
• Experience building AI or GenAI applications, ideally including conversational analytics, intelligent assistants or agentic workflows.
• Experience with BI and visualisation tools, cloud data platforms, data lakes/ADL and enterprise data environments.
• Understanding of data governance, model governance, responsible AI, deployment and monitoring practices.
• Strong commercial and financial acumen, with the ability to connect analytical findings to performance, decisions and actions.
• Ability to turn complex data science and AI outputs into simple, compelling executive stories.
• High ownership and a builder mindset, comfortable moving rapidly from idea to MVP to scaled solution.
• Strong stakeholder management, communication and influencing skills.
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Statistics, Econometrics, Engineering, Mathematics or another quantitative discipline.

Nice to have

• Additional education or demonstrated hands-on expertise in machine learning, GenAI, LLMs or AI-product development.

Details

• Location: Bangalore.
• Office-based teams work in person at least 3 days per week; onsite roles require full-time presence in company facilities.