Key Responsibilities

Build and maintain lead scoring models to prioritize high-intent customers/prospects for sales and marketing teams
Develop churn prediction models to identify at-risk customers and support retention strategies
Design and execute Media Mix Modeling (MMM) studies to measure channel-level marketing effectiveness and guide budget allocation
Write effective SQL queries to extract, transform, and validate data from large marketing and transactional datasets
Build and productionize data pipelines and models in Python (pandas, scikit-learn, statsmodels, or equivalent)
Work within Google Cloud Platform (GCP) — BigQuery, Cloud Run/Functions, IAM, and related services — to build scalable data workflows
Translate model outputs into clear, business-relevant recommendations for marketing and media teams
Collaborate with client servicing, media planning, and analytics teams to embed data science outputs into campaign and business decisions
Stay current on marketing measurement approaches (attribution, incrementality, media effectiveness) and apply them to client problems





Required Skills & Experience

Strong proficiency in Python for data analysis and machine learning
Solid SQL skills, comfortable working with large, complex datasets
Working knowledge of GCP, particularly BigQuery; familiarity with cloud-based data pipelines
Practical experience building lead scoring and churn prediction models
Experience with or strong conceptual understanding of Media Mix Modeling (e.g., adstock, saturation curves, Bayesian approaches such as Meridian, Robyn, or PyMC-Marketing is a plus)
Good understanding of media and marketing concepts — campaigns, channels, funnels, attribution, and marketing KPIs
Ability to communicate technical findings to non-technical stakeholders, including clients
Willingness to build expertise in Google Ads Data Hub (ADH), or prior exposure to ADH study types (Frequency Analysis, Brand Lift, Path to Conversion, etc.)

Good To Have

Experience with

📌 Data Scientist- CSA (Mumbai)
🏢 PivotRoots
📍 Mumbai