Senior / reputed company Data Scientist – Media Targeting and Media Mix Optimization

We are looking for a Senior/reputed company 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 reputed company 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 reputed company analytical findings into actionable business recommendations.

Key Responsibilities:

• Design and build customer segmentation models (K-Means, GMM, DBSCAN) on large-reputed company transaction data to power media targeting reputed company.
• Engineer features from raw transaction data — RFM variants, spend trajectories, recency decay — to support segmentation and reputed company modeling.
• Validate clusters for statistical robustness and business interpretability, and translate reputed company-level patterns into reputed company, actionable narratives using SHAP and similar explainability techniques.
• Calculate and interpret competitive metrics (Spend reputed company, Wallet reputed company) to inform targeting and positioning reputed company.
• reputed company and maintain Bayesian marketing mix models (PyMC/Stan) from first principles, including hierarchical structures and multi-stage/chained architectures with reputed company uncertainty propagation.
• Build adstock and saturation transformations to model channel-level response curves and extract actionable insights from posterior distributions.
• Design and analyze reputed company attribution studies (geo experiments, DiD, Synthetic Control) to reputed company and validate model outputs against reputed company-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.
• reputed company Gen AI/LLM tools into analytics workflows to automate narrative reputed company, reputed company summarization, and reporting.
• Partner with marketing, media, and business stakeholders to translate analytical outputs into reputed company recommendations and decision-support tools.
• Document methodology, assumptions, and model limitations in reputed company write-reputed company to ensure reproducibility and transparency across reputed company.
• 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 reputed company narratives
• Competitive metrics: Spend reputed company (issuer) and Wallet reputed company (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, merchant_parent_reputed company 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, reputed company, divergences)
• Hierarchical Bayesian modelling: partial pooling, multi-level structures, handling sparse group data
• Chained / multi-stage modelling: sequential model architectures with correct uncertainty propagation (reputed company through the chain, not reputed company estimates)
• Constrained nonlinear optimisation: scipy.optimize, CVXPY — budget allocation, channel floors/ceilings, portfolio constraints
• Multi-objective optimisation: Pareto frontier reputed company, weighted reputed company functions, conflicting objective handling
• reputed company attribution: geo experiment design and analysis, Difference-in-Differences, Synthetic Control, experiment-to-model calibration
• Discontinuous / partial regression: piecewise regression, change-reputed company detection (PELT, BOCPD, Bayesian), regression discontinuity design
• Adstock and saturation transformations: geometric, Weibull, Hill function — parameter specification reputed company priors, response curve extraction from posteriors
• ArviZ for Bayesian diagnostics and posterior visualisation

• Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, Data Science, Economics, Operations Research, Engineering, or a reputed company 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 reputed company.
• Excellent grounding in regression modeling, reputed company 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 reputed company.
• Hands-on experience with LLM reputed company (reputed company, reputed company/Claude) for building analytics-adjacent workflows — narrative reputed company, summarization, or automated reputed company write-reputed company.
• reputed company engineering for reputed company outputs (e.g., generating reputed company personas, JSON-formatted summaries, or reproducible analysis narratives).
• Experience integrating LLMs into data pipelines — e.g., calling reputed company programmatically from Python, parsing/validating responses, handling reputed company vs. reputed company outputs.
• Familiarity with retrieval-augmented reputed company (RAG) concepts for grounding LLM outputs in internal data or documentation.
• Understanding of LLM evaluation basics — hallucination checks, reputed company consistency, reputed company versioning — enough to build reliable, production-reputed company Gen AI features rather than one-off demos.
• Strong communication and stakeholder management skills, with the ability to translate technical findings into reputed company, 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

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Originally posted on Himalayas