Agency :
PivotRoots
Job Description :
Havas CSA is looking for a Data Scientist to join our analytics team, working on marketing and media analytics problems across FMCG, BFSI, retail, and automotive clients. The role blends hands-on statistical/ML modeling with a strong understanding of media and marketing performance and requires the ability to translate business questions into data solutions that clients can act on. 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 productive 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 approac
📌 Data Scientist- CSA (Mumbai)
🏢 Havas
📍 Mumbai
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