Key Responsibilities

- Develop, validate, and deploy advanced regression-based frameworks to measure channel and campaign ROI.
- Solve complex modeling problems, including interaction effects, mediation effects, nonlinear relationships, spline- and GAM-based models, hierarchical structures, and time-varying coefficients.
- Research, adapt, and implement methods from academic and industry literature in causal inference, Bayesian modeling, marketing science, experimentation, and time-series forecasting.
- Design and conduct parameter-recovery studies, simulation exercises, sensitivity analyses, and other evaluation frameworks to assess model identifiability, robustness, and reliability.
- Design and analyze A/B tests, multivariate experiments, geo-experiments, and other causal studies to generate actionable business insights.
- Collaborate with product, engineering, and marketing science teams to understand business needs, translate them into technical requirements, and integrate measurement models into scalable production systems.
- Contribute to internal research initiatives and author technical papers, white papers, and methodological documentation that communicate the organization s scientific work.
- Participate actively in regular Scientific Council meetings, collaborating with academic advisers and internal research teams to review methodologies, challenge assumptions, and shape the research agenda.
- Continuously evaluate and apply relevant AI, machine learning, and statistical techniques to improve the quality, scalability, and differentiation of the measurement platform.
- Establish and promote best practices in data science, causal inference, experimentation, model evaluation, and statistical rigor across the organization.




- Mentor junior data scientists and contribute to a culture of continuous learning, scientific curiosity, and methodological innovation.

What We Are Looking For

Skills:

- Master s or PhD in Computer Science, Statistics, Mathematics, Econometrics, or a related quantitative field.
- Solid expertise in causal inference techniques, including difference-in-differences, synthetic control, instrumental variables, causal graphs, mediation analysis, and Bayesian causal modeling.
- Deep understanding of marketing science and measurement, including marketing mix modeling, incrementality testing, attribution, customer lifetime value prediction, and marketing experimentation.
- Proven track record in building complex regression-based models, including hierarchical or multilevel models, regularized regression, Bayesian regression, andtime-varying models.
- Ability to connect causal inference and statistical modeling approaches to practical marketing decisions such as budget allocation, channel optimization, customer acquisition, retention, and long-term value creation.
- Hands-on experience with experimentation design, A/B testing, geo-experiments, and uplift modeling.
- Proficiency in Python or R, SQL, and cloud-based data platforms.
- Experience deploying models into production and working with large-scale data pipelines.
- Ability to read, interpret, and translate academic research papers into practical, scalable implementations.

Experience

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

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Data Scientist (Hubballi)
🏢 Lifesight
📍 Hubballi