Ipsos' Creative Excellence department is looking to evolve its AI-enabled capability, Creative Spark AI, which predicts and explains ad performance. This evolution includes creating new products to capitalize on market opportunities and enhance insight generation.
Requirements
- Computer Vision, GenAI and NLP
- Strong proficiency in Python and the main data & ML libraries (e.g. pandas, NumPy, scikit-learn, plus optionally TensorFlow / PyTorch / CatBoost / XGBoost).
- Good working knowledge of SQL and experience querying large analytical datasets (e.g. in BigQuery 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 issues.
- Experience with the following: NLP or Computer Vision applied to creatives (scripts, storyboards, video / image / audio).
- Experience with the following: Cloud platforms, ideally Google Cloud Platform (GCP).
- Experience with the following: Experiment tracking and MLOps tools (e.g. MLflow, model registries, CI/CD for ML).
Responsibilities
- Design, engineer, and test new model variants from survey, coded, or digital data sources, to improve prediction accuracy and explainability of ad performance across distinct ad environments and verticals.
- Integrate new features into experimental models and quantify their impact on prediction accuracy, robustness, and interpretability, and summaries the uplift (or lack thereof) for CRE senior management decision-making.
- Document feature definitions, derivation logic, and performance impact for replicability
- Design and execute experiments and benchmarks comparing different feature sets, algorithms, or model configurations (e.g. classical ML, deep learning, NLP / CV approaches).
- Use appropriate evaluation metrics (e.g. accuracy, AUC, RMSE, calibration, stability across segments) and validation schemes (cross-validation, hold-out, time-based splits) to ensure robust conclusions.
- Maintain clear experiment logs and documentation (notebooks, reports, dashboards) so results can be reviewed, reproduced, and reused by CRE and GADS teams.
- Contribute to continuous improvement of modelling best practices for Creative|Spark AI.
Other
- Master’s degree (or equivalent) in Data Science, Statistics, Applied Mathematics, Computer Science, Econometrics, or a related quantitative field.
- 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
- Prior exposure to production or near-production environments (e.g. working on models that are deployed, monitored, and iterated).
