Overview
Senior/Lead Data Scientist to design, enhance, and optimize marketing investment strategies using statistical modeling, machine learning, optimization, and explainability.
What you'll do
- Design and build customer segmentation models (K-Means, GMM, DBSCAN) on large-scale transaction data.
- Engineer features from transaction data (RFM variants, spend trajectories, recency decay) to support segmentation and modeling.
- Validate clusters using statistical robustness and business interpretability, and create actionable narratives using SHAP.
- Develop Bayesian marketing mix models in PyMC/Stan, including hierarchical and multi-stage structures with uncertainty propagation.
- Model channel response with adstock and saturation transformations and extract insights from posterior distributions.
- Run causal attribution studies (geo experiments, DiD, Synthetic Control) to calibrate outputs against real-world lift.
- Recommend budget allocations using constrained and multi-objective optimization and disaggregate budgets into monthly/channel plans.
- Integrate LLM tools into analytics workflows for narrative generation, insight summarization, and reporting.
What you'll need
- 5+ years of hands-on experience in data science, marketing analytics, media/audience targeting, or marketing mix optimization.
- Strong Python programming experience and solid SQL skills for data extraction and analysis at scale.
- Strong grounding in regression modeling, applied statistics, machine learning, feature engineering, and model validation.
- Experience building optimization or decision-support models for budget allocation, scenario planning, or media mix decisions.
- Hands-on experience with LLM APIs (OpenAI, Anthropic/Claude) and prompt engineering for structured outputs.
- Technical knowledge of Python, SQL, Pandas, NumPy, and scikit-learn.
- Strong communication and stakeholder management to translate technical findings into actionable recommendations.
Read the full description and apply on the company’s own careers page.
