Data Scientist, Marketing Analytics

Role overview

Data Scientist, Marketing Analytics at Ford Motor Private Limited

In this role, you'll partner with your US based data science peers, data engineering, and marketing teams to turn customer and campaign data into measurable insight — driving optimization, measurement, and growth across email, paid media, and multi-channel journeys.

Responsibilities

• Build causal inference models and A/B/n test designs to quantify campaign impact Support marketing campaign performance forecasting and target setting.

Preferred qualifications and skills

• Bachelor's degree in a quantitative field; advanced degree preferred.

Employment details

• Location: Chennai [India]

Additional role details

• We're looking for a Senior Data Scientist to drive the analytics and measurement strategy across our CRM marketing and performance media programs for North America.
• Design and own measurement frameworks for CRM (email, push, lifecycle) and performance media (paid search, social, display) campaigns, including incrementality, holdouts, and lift measurement.
• Partner with agencies and data partners to ensure tracking, data quality, and a consistent measurement taxonomy across owned and paid channels.
• Translate complex analyses into clear, actionable recommendations for non-technical stakeholders.
• Leverage and evaluate emerging AI tools and capabilities to streamline existing workflows and unlock new analytical approaches — automating reporting, assisting with code and feature engineering, and enabling faster, higher-quality insights.
• Mentor junior analysts/data scientists on analytics best practices. 5+ years of applied data science experience, with a focus on CRM marketing and/or performance media analytics Direct experience working with media platform data (Google Ads, Search Ads 360, Meta, DV360, etc.) and combining it with internal customer and sales data for daily optimization.
• Strong understanding of CRM metrics (engagement, churn, LTV, lifecycle) and media performance signals (efficiency, cost, conversion, ROAS).
• Ability to partner across teams (e.g., MMM, media buying, CRM) to connect the dots and deliver a holistic performance view.
• Strong statistical grounding: experimental design, causal inference, predictive modeling, time-series.
• Curiosity and hands-on comfort with AI/ML tooling and automation (e.g., LLMs, copilots, auto-ML) to speed up data work.
• Fluency in SQL and Python/R.
• Excellent communication — able to influence daily decisions with data.