Data Scientist, Creative Excellence

Data Scientist, Creative Excellence

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Within Ipsos, the Creative Excellence helps clients understand what makes advertising effective across TV, digital, social, and other media channels. A core strategic solution is Creative Spark AI, an AI‑enabled capability that predicts and explains ad performance at scale and globally. Your primary focus will be to support the development and evolution of this solution and to create new products using its framework to capitalize on emerging market opportunities.

• Feature and model development: design, engineer, and test new model variants from survey, coded, or digital data sources to improve prediction accuracy and explainability.
• Integrate new features into experimental models and quantify their impact on prediction accuracy, robustness, and interpretability, summarizing insights for senior management.
• Document feature definitions, derivation logic, and performance impact for replicability.
• Experimentation, evaluation, and documentation: design and execute experiments and benchmarks comparing different feature sets, algorithms, or model configurations; use appropriate evaluation metrics and validation schemes; maintain clear experiment logs and documentation.
• Continuous improvement of modelling best practices for Creative Spark AI.
• Advocate for and own new products and solutions that generate incremental revenue.
• Develop an understanding of Ipsos’ Creative Excellence business and translate this into new solutions aligned with global and U.S. product teams.
• Lead the transformation of the business model through the strategic use of synthetic data and enhance insight generation.
• Communicate modelling results, feature impacts, and recommendations in clear, non‑technical language to stakeholders; collaborate with business‑facing teams to refine client questions and ensure methodological rigor.
• Support client‑facing presentations or proposals with concise, well‑structured analytical inputs; contribute to internal training, playbooks, and knowledge sharing.

• Master’s degree (or equivalent) in Data Science, Statistics, Applied Mathematics, Computer Science, Econometrics, or a related quantitative field.
• Ph.D. is a plus but not required.

• 7‑10 years of professional experience as a Data Scientist in applied machine learning.
• Hands‑on experience building and evaluating supervised learning models (regression / classification) in real‑world use cases.
• Experience in product management or technical lead roles is a plus.
• Experience in at least one of the following: marketing, advertising, media, or market research; or predictive modelling on survey, panel, or customer behavior data.
• Prior exposure to production or near‑production environments (e.g., models that are deployed, monitored, and iterated).

• Computer Vision, GenAI, and NLP expertise.
• Strong proficiency in Python and core data & ML libraries (pandas, Num Py, scikit‑learn, optionally Tensor Flow, PyTorch, Cat Boost, XGBoost).
• Good working knowledge of SQL and experience querying large analytical datasets (e.g., Big Query or similar cloud warehouses).
• Demonstrated understanding of core ML concepts: feature engineering, regularization, model selection, cross‑validation, evaluation metrics for regression / classification, bias, overfitting, drift, and robustness.
• Experience with: NLP or Computer Vision applied to creatives; cloud platforms, ideally Google Cloud Platform; experiment tracking and MLOps tools (e.g., MLflow, model registries, CI/CD for ML).
• Strong analytical and problem‑solving skills with attention to detail and methodological rigor.
• Ability to translate business and research problems into concrete analytical approaches.
• Comfortable working in cross‑functional teams (data science, engineering, research, client service).
• Curiosity, pragmatism, and a willingness to learn from all colleagues.
• Ability to work autonomously on clear work streams while seeking feedback when needed.
• Excellent communication skills.

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