Requirements Summary

Strong expertise in Media Targeting and Media Mix Optimization, with experience building scalable optimization solutions and translating complex analytical findings into actionable business recommendations

Benefits

Education

Master's degree

Full Job Description

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. What You Will Do Key responsibilities include designing and building customer segmentation models, engineering features from raw transaction data, validating clusters, calculating and interpreting competitive metrics, developing and maintaining Bayesian marketing mix models, and building adstock and saturation transformations. Why It Might Be a Fit The ideal candidate will have experience building scalable optimization solutions that help maximize marketing ROI and improve budget allocation across channels, and will be able to translate complex analytical findings into actionable business recommendations. 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 Dynamic Career Growth Idea Tanks Growth Chats Snack Zone Recognition & Rewards Fuel Your Growth Journey with Certifications

Skills and Technologies