Senior Lead Data Scientist – Media Targeting and Media Mix Optimization

We are looking for a Senior/Lead Data Scientist with strong expertise in Media Targeting and Media Mix Optimization  to design enhance and optimize marketing investment strategies using advanced statistical modeling machine learning and optimization techniques. The ideal candidate will have experience building scalable optimization solutions that help maximize marketing ROI and improve budget allocation across channels.

This role requires excellent Python programming skills strong statistical foundations and the ability to translate complex analytical findings into actionable business recommendations.

Key Responsibilities:

• Design and build customer segmentation models (K-Means GMM DBSCAN) on large-scale transaction data to power media targeting strategy.
• Engineer features from raw transaction data  RFM variants spend trajectories recency decay  to support segmentation and downstream modeling.
• Validate clusters for statistical robustness and business interpretability and translate segment-level patterns into clear actionable narratives using SHAP and similar explainability techniques.
• Calculate and interpret competitive metrics (Spend Index Wallet Share) to inform targeting and positioning decisions.
• Develop and maintain Bayesian marketing mix models (PyMC/Stan) from first principles including hierarchical structures and multi-stage/chained architectures with proper uncertainty propagation.
• Build adstock and saturation transformations to model channel-level response curves and extract actionable insights from posterior distributions.
• Design and analyze causal attribution studies (geo experiments DiD Synthetic Control) to calibrate and validate model outputs against real-world lift.
• Build constrained and multi-objective optimization models (scipy CVXPY) to recommend budget allocations across channels respecting business constraints and floors/ceilings.
• Disaggregate coarse budget plans into monthly/channel-level media plans using temporal disaggregation techniques.
• Integrate Gen AI/LLM tools into analytics workflows to automate narrative generation insight summarization and reporting.
• Partner with marketing media and business stakeholders to translate analytical outputs into clear recommendations and decision-support tools.
• Document methodology assumptions and model limitations in structured write-ups to ensure reproducibility and transparency across the team.
• Work independently against a defined brief proactively flagging risks data gaps or blockers to stakeholders.

Segmentation & Media Targeting

• Customer segmentation: K-Means GMM DBSCAN understands the underlying mathematics not just the API
• Cluster validation: silhouette score stability testing business interpretability
• Feature engineering on transaction data: multi-window RFM spend trajectory recency decay time spine construction
• SHAP explainability: interpreting feature importance and translating it into plain-English segment narratives
• Competitive metrics: Spend Index (issuer) and Wallet Share (merchant) calculation and correct interpretation including network coverage limitations
• Temporal disaggregation: Denton-Cholette or equivalent distributing coarse budgets into monthly media plans
• Media budget allocation logic: heuristic channel splits and MMO response curve integration
• Data filtering: BIN/ICA logic for issuer data merchantparentname for merchant data

Bayesian Modelling & Optimisation

• Bayesian regression: model specification from scratch (likelihood priors hierarchy) not just library calls
• PyMC and/or Stan: model building MCMC sampling convergence diagnostics (R-hat ESS divergences)
• Hierarchical Bayesian modelling: partial pooling multi-level structures handling sparse group data
• Chained / multi-stage modelling: sequential model architectures with correct uncertainty propagation (Monte Carlo through the chain not point estimates)
• Constrained nonlinear optimisation: CVXPY budget allocation channel floors/ceilings portfolio constraints
• Multi-objective optimisation: Pareto frontier generation weighted utility functions conflicting objective handling
• Causal attribution: geo experiment design and analysis Difference-in-Differences Synthetic Control experiment-to-model calibration
• Discontinuous / partial regression: piecewise regression change-point detection (PELT BOCPD Bayesian) regression discontinuity design
• Adstock and saturation transformations: geometric Weibull Hill function parameter specification via priors response curve extraction from posteriors
• ArviZ for Bayesian diagnostics and posterior visualisation

Qualifications :

• Bachelors or Masters degree in Statistics Mathematics Computer Science Data Science Economics Operations Research Engineering or a related quantitative field.
• 5 years of hands-on experience in Data Science Marketing Analytics Media/Audience Targeting or Marketing Mix Optimization.
• Strong programming experience in Python with solid SQL skills for data extraction and analysis at scale.
• Excellent grounding in regression modeling applied statistics machine learning feature engineering and model validation.
• Experience working with large marketing sales or transaction-level datasets including customer segmentation and audience targeting.
• Experience developing optimization or decision-support models for budget allocation scenario planning or media mix decisions.
• Hands-on experience with LLM APIs (OpenAI Anthropic/Claude) for building analytics-adjacent workflows narrative generation summarization or automated insight write-ups.
• Prompt engineering for structured outputs (e.g. generating segment personas JSON-formatted summaries or reproducible analysis narratives).
• Experience integrating LLMs into data pipelines e.g. calling APIs programmatically from Python parsing/validating responses handling structured vs. unstructured outputs.
• Familiarity with retrieval-augmented generation (RAG) concepts for grounding LLM outputs in internal data or documentation.
• Understanding of LLM evaluation basics hallucination checks output consistency prompt versioning enough to build reliable production-safe Gen AI features rather than one-off demos.
• Strong communication and stakeholder management skills with the ability to translate technical findings into clear actionable recommendations.

Technical Skills Must Have

• Python SQL Pandas NumPy scikit-learn
• Statistical modeling and regression analysis
• Marketing analytics and audience/media targeting
• Marketing Mix Optimization (MMO) and budget optimization
• Scenario planning and predictive modeling
• Customer segmentation and clustering techniques
• AI/LLM integration and evaluation for analytics and reporting workflows

Additional Information :

Thrive & Grow with Us

Competitive Salary: Your skills and contributions are highly valued here and we make sure your salary reflects that rewarding you fairly for the knowledge and experience you bring to the table.

Dynamic Career Growth: Our vibrant environment offers you the opportunity to grow rapidly providing the right tools mentorship and experiences to fast-track your career.

Idea Tanks: Innovation lives here. Our Idea Tanks are your playground to pitch experiment and collaborate on ideas that can shape the future.

Growth Chats: Dive into our casual Growth Chats where you can learn from the bestwhether its over lunch or during a laid-back session with peers its the perfect space to grow your skills.

Snack Zone: Stay fuelled and inspired! In our Snack Zone youll find a variety of snacks to keep your energy high and ideas flowing.

Recognition & Rewards: We believe great work deserves to be recognized. Expect regular Hive-Fives shoutouts and the chance to see your ideas come to life as part of our reward program.

Fuel Your Growth Journey with Certifications: Were all about your growth groove! Level up your skills with our support as we cover the cost of your professional certifications.

Remote Work :

Yes

Employment Type :

Full-time