Data Scientist, Creative Excellence

About the position 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, which is an AI-enabled capability to predict and explain ad performance, at scale and globally. Your primary focus will be to support the development and evolution of this solution. This evolution will also include creation of new products utilizing the framework to take advantage of current blue ocean space in the market. What We Offer The opportunity to work with cutting-edge AI and data technologies in a production environment, in partnership with other experienced data scientists and engineers within the Generative AI & Data Science department. A culture that values curiosity, scientific rigor, collaboration, and continuous learning. The chance to grow towards more senior or specialized roles (e.g. lead data scientist, AI product specialist, or domain expert in creative analytics). Role Summary In this role, you will contribute to the design and experimentation of the AI models and features that power Creative|Spark AI and related solutions. When client and industry need demand it, you will experiment to find alternative measurement and modelling best practices which may become independent products or solutions. You will ensure that experimentation and custom analyses remain consistent with our Creative|Spark AI global methodology, where applicable. You will translate research and client briefs into modelling problems (which data, which features, which modelling approach, which evaluation criteria) and ensure actionable insights and recommendations. You are expected to work autonomously on well-defined problems, collaborate closely with lead data scientists and engineers, and progressively take ownership of more complex modelling workstreams. Responsibilities • Feature and Model Development 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 • Experimentation, Evaluation & Documentation 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. • Act as an advocate for, and owner of, new products and solutions that grow incremental revenue on top of CRE’s core, traditional business Develop an understanding of Ipsos’ Creative Excellence business and its evolution into new spaces. Translate this understanding into new solutions, along with global and US product teams, which answer client questions consistently, efficiently and accurately utilizing ML, Gen AI and survey methods • Drive the transformation of Ipsos ‘s business model through the strategies use of synthetic data Enhance insight generation, augmenting traditional data methods, and enabling advanced market simulations Support innovative product development, new revenue streams, and greater value from data assets. • Communication & Stakeholder Engagement Help translate stakeholder business and research questions into robust, documented analytical workflows, aligned with Ipsos’ methodologies and AI governance. Present modelling results, feature impacts, and recommendations in a clear, non-technical language to CRE stakeholders. Collaborate with business-facing teams to frame and refine client questions, ensuring feasibility and methodological rigor. Contribute to internal training, playbooks, and knowledge sharing on our core AI solution. When relevant, support client-facing presentations or proposals with concise, well-structured analytical inputs. Requirements • 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. • 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. • Strong analytical and problem-solving skills, with attention to detail and methodological rigor. • Ability to understand business and research problems and translate them into concrete analytical approaches. • Comfortable working in cross-functional teams (data science, engineering, research, client service). • Excellent communication skills Nice-to-haves • PhD is a plus but not required for this role. • 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. working on models that are deployed, monitored, and iterated). • Experience with the following: NLP or Computer Vision applied to creatives (scripts, storyboards, video / image / audio). • Cloud platforms, ideally Google Cloud Platform (GCP). • Experiment tracking and MLOps tools (e.g. MLflow, model registries, CI/CD for ML). • Curious, pragmatic, and eager to learn from both technical and non-technical colleagues. • Able to work autonomously on clearly defined workstreams, while actively seeking feedback when needed. Benefits • Career Development • an exceptional benefits package (including generous PTO, healthcare plans, wellness benefits) • a flexible workplace policy • a strong collaborative culture Apply tot his job