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Senior / Lead Data Scientist – Media Targeting and Media Mix Optimization

Blend360

IndiaremotePosted 1 month ago
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Skill Required

Data-ScienceMarketing-AnalyticsMedia-Mix-OptimizationCustomer-SegmentationMartechSenior-Data-Scientist-IIISenior-Machine-Learning-Data-ScientistData-ScientistData ScientistandGenerative AIMachine LearningEngineeringR ProgrammingStatisticsanalyticalanalyticsbuildingTestNGPythondesignOpenAPIDesign PatternsNumPyAPIsSQLAIFulltime

Key highlights

  • Senior/Lead Data Scientist role focused on Media Targeting and Media Mix Optimization
  • 5+ years of hands-on experience required in Data Science, Marketing Analytics, Media/Audience Targeting, or Marketing Mix Optimization
  • Bachelor's or Master's degree required in a quantitative field (Statistics, Mathematics, Computer Science, Data Science, Economics, Operations Research, Engineering, or related)
  • Competitive salary with certification cost coverage as a key benefit
  • Strong Python and SQL programming skills required, with hands-on LLM API experience (OpenAI, Anthropic/Claude) listed as a must-have

Role overview

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.

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.

Requirements

  • Bachelor's or Master's 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.
  • 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.
  • 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, merchant_parent_name for merchant data.
  • 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: scipy.optimize, 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.

Benefits

  • Competitive salary that reflects skills, contributions, knowledge, and experience.
  • Dynamic career growth with tools, mentorship, and experiences to fast-track career progression.
  • Idea Tanks: playground to pitch, experiment, and collaborate on innovative ideas.
  • Growth Chats: casual learning sessions with peers over lunch or laid-back sessions.
  • Snack Zone: variety of snacks to keep energy and ideas flowing.
  • Recognition & Rewards: regular Hive-Fives, shoutouts, and a reward program where ideas come to life.
  • Certification support: company covers the cost of professional certifications.

Additional details

  • Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people.
  • The company's mission is to fuel bold visions and tackle significant challenges by seamlessly aligning human expertise with artificial intelligence.
  • Blend is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy.
  • The company believes that the power of people and AI can have a meaningful impact on the world, creating more fulfilling work and projects for their people and clients.
  • Originally posted on Himalayas.
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