AI Marketing ROI Forecasting Scientist

As an AI Marketing ROI Forecasting Scientist at Cehpoint, you will own the end-to-end development of predictive and causal models that quantify how every marketing dollar translates into measurable business outcomes. Your work will directly shape multimillion-dollar budget allocations across our enterprise clients, bridging rigorous statistical science with actionable growth insights.

• Design and implement marketing mix models (MMM), multi-touch attribution (MTA), and uplift/causal inference models to forecast campaign ROI and incrementality.
• Build and maintain scalable feature pipelines ingesting ad spend, clickstream, CRM, web analytics, and macroeconomic signals from cloud data warehouses.
• Conduct rigorous experiment design, power analysis, and causal identification strategies; validate model predictions against holdout markets and randomized campaigns.
• Develop Bayesian hierarchical and probabilistic ML models to quantify uncertainty, detect diminishing returns, and segment ROI by channel, geography, and audience cohort.
• Translate model outputs into executive-ready dashboards and narrative reports, partnering with marketing leadership to convert forecasts into budget-recommendation playbooks.
• Stay current with frontier research in marketing science, LLM-augmented attribution, and privacy-safe measurement frameworks; prototype and productionize promising approaches.

• Causal inference foundations — potential outcomes, propensity scoring, instrumental variables, difference-in-differences, and their marketing-science applications.
• Bayesian statistics & probabilistic programming — hierarchical models, MCMC/VI inference, posterior predictive checks, and uncertainty quantification for decision-making.
• Marketing science fundamentals — media-mix modeling, brand-lift experimentation, funnel attribution, and the interplay between short-term performance and long-term brand equity.

• Python — scikit-learn, XGBoost/LightGBM, Prophet, PyMC/Stan, statsmodels
• SQL & cloud data platforms — BigQuery or Snowflake, dbt for transformation layers
• Experimentation tooling — R or Python for A/B-test analysis, power & sample-size calculations
• Visualization & communication — Python (Matplotlib, Seaborn, Plotly) and BI tools (Tableau/Power BI/Looker)
• MLOps & version control — Git, MLflow or equivalent, Docker for reproducible model packaging

• Relentlessly questions whether a correlation is truly causal before recommending spend reallocation — digs into confounders and selection bias.
• Explores unconventional data sources (geolocation, weather, event calendars, competitor signals) to extract marginal predictive value for ROI models.
• Continuously challenges the status quo of traditional attribution by prototyping privacy-preserving measurement techniques and LLM-augmented insights.

• Competitive annual compensation (₹12L–₹20L) with performance-linked bonuses and early equity eligibility.
• Fully remote setup with a home-office stipend and flexible hybrid days at our Bengaluru headquarters.
• Access to dedicated GPU credits and premium cloud compute for model training and experimentation.
• Structured mentorship from senior data scientists and marketing-science leads, plus annual conference budget for NeurIPS, WSDM, or INMA.
• Health insurance covering self and family, generous leave policy, and a sabbatical program after 4 years of service.