Senior Data Scientist

Senior Data Scientist-Marketing and Consumer Analytics

About the Role

We are looking for experienced analytics professional to spearhead our Marketing & Consumer Analytics practice within the Data Science team. This role will own the strategy, delivery, and team leadership for high-impact analytics solutions spanning pricing, promotions, attribution, segmentation, and personalization across BFSI, Retail, CPG, and Ecommerce clients or business units.

Key Responsibilities

• Lead end-to-end delivery of pricing and promotions optimization, marketing mix modelling (MMM), and multi-touch/incrementality-based attribution projects
• Drive customer segmentation and hyper-personalization initiatives that translate consumer behavior data into actionable business strategies
• Build and scale causal inference and experimentation frameworks (A/B testing, uplift modeling, incrementality testing) to validate marketing and pricing decisions
• Own end-to-end model lifecycle including deployment, monitoring, and governance in production environments
• Mentor and grow a multidisciplinary team of data scientists and analysts, setting technical standards and fostering a culture of experimentation
• Act as the primary analytical advisor to senior business or client stakeholders, translating complex analytical outputs into clear commercial narratives
• Partner with cross-functional teams (marketing, product, business, engineering) to prioritize and scope high-value analytics use cases
• Build internal accelerators across customer and marketing analytics to FastTrack project deployments

Required Skills and Experience

• 7 to 10 years of experience in analytics/data science, with a demonstrated track record developing and leading pricing, promotions, attribution, segmentation, or personalization projects
• Sector experience across BFSI, Retail, CPG, or Ecommerce, ideally in high-transaction-volume, consumer-facing businesses
• Strong grounding in causal inference and econometrics (uplift modeling, panel data methods, difference-in-differences), alongside traditional ML techniques (clustering, propensity modeling, tree-based/neural models)
• Hands-on proficiency in Python/R and SQL for large-scale data manipulation
• Experience with cloud and big data platforms (AWS, GCP, or Azure) and distributed processing frameworks (Spark)
• Familiarity with MLOps practices for deploying and monitoring models in production
• Working knowledge of visualization/BI tools (Tableau, Power BI, Looker, or GA4) for stakeholder reporting
• Proven experience managing and mentoring cross-functional analytics teams
• Prior experience in a consulting, agency, or client-facing services environment is a plusPreferred Qualifications
• Degree in Statistics, Economics, Computer Science, Data Science, or related quantitative field
• Experience with LTV modeling, churn prediction, or full-funnel measurement (acquisition, engagement, retention)
• Exposure to Bayesian modeling frameworks (PyMC3, Stan, or equivalent)
• Strong executive communication and stakeholder management skills