- Develop, validate, and deploy advanced regression-based frameworks to measure channel and campaign ROI.

- Research, adapt, and implement the latest methods from academic and industry papers in causal inference, Bayesian modelling, and time-series forecasting.

- Design and analyse A/B and multivariate experiments to drive actionable business insights.

- Collaborate with cross-functional teams (product, engineering, marketing science) to integrate measurement models into scalable systems.

- Continuously explore and apply the latest AI/ML techniques to maintain a competitive edge.

- Work closely with cross-functional teams to understand business needs and translate them into technical requirements.

- Mentor junior data scientists and contribute to fostering a culture of continuous learning and innovation.

- Establish and evangelise best practices in data science, experimentation, and statistical rigour across the organisation.

Requirements :

- Master's or PhD in Computer Science, Statistics, Mathematics, or a related field.

- Strong expertise in causal inference techniques (DID, synthetic control, instrumental variables, Bayesian causal modelling, etc.)

- Proven track record in building complex regression-based models (hierarchical/multilevel, regularised, Bayesian regression, etc).

- Hands-on experience with experimentation design, A/B testing, and uplift modelling.

- Proficiency in Python/R, SQL, and cloud-based data platforms.

- Experience in deploying models into production and working with large-scale data pipelines.

- Ability to read, interpret, and translate research papers into practical implementations.

- Excellent communication skills to explain complex models and results to both technical and business stakeholders.

- 5+ years of experience in data science or applied statistics, preferably in marketing analytics.