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

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Blend360

WebsiteLinkedInAll Job Openings

501 - 1000 employees

🏥 Healthcare

🏨 Hospitality

✈️ Travel

💰 $100M Private Equity Round on 2022-08

Healthcare • Hospitality • Travel

Blend360 is a professional services company specializing in AI, data analytics, and data-driven solutions. They work with Fortune 1000 and large enterprise brands to tackle significant challenges by integrating people and artificial intelligence. Blend360 focuses on several domains including business intelligence, data engineering, data science, MLOps, and data governance. Their industries of expertise encompass financial services, energy, healthcare and life sciences, retail, technology, media & telecom, and travel & hospitality. Blend360 is recognized for their AI and data solutions, having earned accolades such as "AI-Enabling Solution of the Year" and being listed among the "Top Generative AI Service Providers 2024.

📋 Description

• 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.

🏖️ Benefits

• 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 best—whether it's over lunch or during a laid-back session with peers, it's the perfect space to grow your skills. • Snack Zone: Stay fuelled and inspired! In our Snack Zone, you'll 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: We're all about your growth groove! Level up your skills with our support as we cover the cost of your professional certifications.