Director

Publicis Media; Bengaluru; On-site; INR 3,500,000 - 7,500,000; Full time

Job summary

Publicis Media is seeking an Associate Director / Director of Analytics in Bengaluru to lead high-impact Marketing Mix Modelling, Marketing Effectiveness, econometrics, and advanced analytics engagements for top brands.

The role requires hands-on modelling, causal reasoning, model governance, and strong client and team leadership across multiple markets. 6-10+ years of analytics leadership is expected.

Qualifications

• 8-12+ years of experience in Analytics, Marketing Science, Econometrics, Data Science or Marketing Effectiveness.
• Strong hands-on experience in Marketing Mix Modelling (MMM).
• Proven experience owning large multi-brand / multi-market MMM programmes.
• Strong understanding of both OLS/econometric and Bayesian MMM approaches.
• Deep understanding of model architecture, causal inference, identifiability and multicollinearity.

Responsibilities

• Lead end-to-end MMM and Marketing Effectiveness engagements across brands, markets and categories.
• Review and challenge model specifications, transformations, priors, diagnostics, contribution estimates and ROI outputs.
• Ensure analytical outputs are supported by sound causal reasoning and statistical evidence.
• Lead analytics teams and mentor data scientists across projects and markets.
• Translate complex findings into clear business and investment recommendations for clients.

Skills

Marketing mix modelling (MMM)

Econometrics

Bayesian MMM

Causal inference

Model governance

Stakeholder management

Team leadership

Tools

R

Python

SQL

Job description

Associate Director / Director - Analytics

Experience: 6-10+ years

Function: Analytics / Marketing Science / Measurement

About the Role

We are looking for an experienced Analytics leader to lead high-impact Marketing Mix Modelling (MMM), Marketing Effectiveness, Econometrics and Advanced Analytics engagements for leading brands.

The role requires a strong combination of hands-on modelling expertise, causal reasoning, model governance, client leadership and team management. The ideal candidate should be comfortable challenging analytical assumptions, reviewing models built by teams and determining whether insights are robust enough to support business and marketing investment decisions.

Key Responsibilities

• Lead end-to-end Marketing Mix Modelling (MMM) and Marketing Effectiveness engagements across brands, markets and categories.
• Provide technical leadership on model architecture, including decisions around separate, pooled, hierarchical and partial-pooling approaches.
• Review and challenge model specifications, transformations, priors, diagnostics, contribution estimates and ROI outputs.
• Ensure analytical outputs are supported by sound causal reasoning and statistical evidence.
• Identify and address issues related to endogeneity, reverse causality, confounding, multicollinearity and identifiability.
• Lead Bayesian and frequentist/econometric modelling approaches and ensure appropriate use of uncertainty and diagnostics.
• Evaluate model stability through sensitivity analysis, holdouts, alternative specifications, transformations, priors and time windows.
• Reconcile MMM findings with geo experiments, incrementality studies and other measurement approaches.
• Govern marketing budget optimizers and ensure recommendations are supported by robust evidence and appropriate constraints.
• Distinguish between prediction quality and attribution quality when evaluating models.
• Review and approve models developed by analysts and data scientists before client delivery.
• Lead and mentor analytics/data science teams working across multiple projects and markets.
• Partner with senior client stakeholders to translate complex analytical findings into clear business and investment recommendations.
• Challenge unreasonable client expectations and communicate limitations of the data and methodology effectively.
• Contribute to new business, pitches and development of analytics solutions and methodologies.

What We’re Looking For

• 8-12+ years of experience in Analytics, Marketing Science, Econometrics, Data Science or Marketing Effectiveness.
• Strong hands-on experience in Marketing Mix Modelling (MMM).
• Proven experience owning large multi-brand / multi-market MMM programmes.
• Strong understanding of both OLS/econometric and Bayesian MMM approaches.
• Deep understanding of model architecture, causal inference, identifiability and multicollinearity.
• Strong hands-on understanding of Bayesian concepts including priors, posterior diagnostics, R-hat, ESS, divergences and posterior predictive checks.
• Experience with adstock, saturation, response curves and contribution modelling.
• Strong understanding of endogeneity, reverse causality, confounders and mediators.
• Experience reconciling MMM results with experimentation / geo testing / incrementality studies.
• Experience with model stability, sensitivity testing and model governance.
• Experience reviewing and approving models developed by other analysts/data scientists.
• Strong client-facing and stakeholder management experience.
• Proven experience leading and mentoring analytics teams.