Senior Data & AI Scientist

**Senior Applied Data Scientist — Marketing Mix Modeling** Opella is a global consumer healthcare company headquartered in Paris, France, and one of the world's largest players in the OTC and self-care market. With around 11,000 employees, 13 manufacturing sites, and over 100 trusted brands — including Allegra, Doliprane, and Enterogermina — the company delivers science-based health solutions to consumers worldwide. Following its 2025 transition to a standalone business, Opella continues to expand its leadership in self-care, driven by innovation, digitalization, and responsible growth.  **About the Team**  The Advertising & Promotion (A&P) Analytics team builds the data science and decision-support systems that guide how Opella invests its global marketing budget across brands, channels, and markets. We combine **predictive and causal modeling**, **scenario analysis**, and **mathematical optimization** to turn measurement into action — helping marketing, finance, and brand stakeholders evaluate trade-offs and commit to multi-million-euro investment decisions. Our work spans the full lifecycle: from modeling, through scenario and what-if engines, to production APIs and stakeholder-facing tools.  **Who You Are**  You are a senior data scientist who thrives at the intersection of **modeling, optimization, and product thinking**, comfortable owning complex problems end-to-end — from framing the business question, to choosing the right modeling approach, to shipping a robust system that stakeholders actually use. You partner closely with engineers, product, and business stakeholders, moving comfortably between deep technical work and stakeholder conversations — translating modeling choices into business trade-offs, and business questions into well-scoped problems. You enjoy challenging the status quo to make sure Opella's AI solutions are scientifically sound, operationally reliable, and impactful for the patients and consumers of tomorrow.  **Job Highlights**  * **Own problems end-to-end**: from framing and modeling, through experimentation and productionization, to monitoring and adoption.  * **Design and build decision systems** combining predictive/causal modeling, scenario analysis, and optimization — including planning and what-if tools that let stakeholders evaluate trade-offs across channels, brands, markets, and constraints.  * **Productionize modeling and optimization engines and APIs** with a focus on robustness, performance, and interpretability.  * Apply expertise in **machine learning, statistics, time-series forecasting, optimization, and Generative AI**, and use **data analysis, visualization, and storytelling** to scope, define, and deliver AI-based data products.  * Contribute to the team's technical direction through **code review, design discussions, and knowledge sharing**, partnering closely with product, engineering, MLOps, and business stakeholders.  * **Invest in long-term code health**: refactor where foundations need strengthening, pay down technical debt, and prefer maintainable, well-tested solutions over short-term workarounds.  **Key Functional Requirements & Qualifications**  * Hands-on AI/ML modeling experience with complex datasets, with strong theoretical grounding in most of: **supervised/unsupervised learning, Bayesian statistics, mathematical optimization (LP/MILP, heuristics), simulation and what-if analysis**.  * Demonstrated experience designing and shipping **decision-support systems** end-to-end (not just notebooks) — production code, APIs, monitoring, and stakeholder adoption — in agile, product-focused environments.  * Experience delivering data science projects in **commercial, operational, or planning domains** — for example marketing analytics, forecasting, recommender systems, supply/manufacturing, or pricing — is a strong plus.  * Comfortable in **cloud and high-performance computing environments** (AWS preferred; also Databricks, Azure).  * Excellent written and verbal communication, business analysis, and **data storytelling** — able to translate technical work for business audiences — and a demonstrated ability to **collaborate effectively** in cross-functional teams (data scientists, engineers, MLOps, product, business).  * Previous experience in business areas such as **Marketing, Finance, Manufacturing & Supply, or Operations**.  * _Nice to have:_ experience in **life sciences, healthcare, or CPG**, and in a complex global organization.  **Key Technical Requirements & Qualifications**  **Education**  * **PhD** in a quantitative discipline (mathematics, computer science, operations research, engineering, physics, statistics, economics, or similar) with strong coding skills, **OR Master's** in a relevant domain with **4+ years** of analytical / applied data science experience.  **Optimization & Operations Research**  * Familiarity with mathematical optimization concepts and tooling (e.g. **Pyomo, OR-Tools, Gurobi**, or similar). Hands-on experience shipping optimization-based decision systems is a plus.  **Statistics & Causal Inference**  * Solid grounding in **statistical modeling and inference**. Exposure to **causal inference** (e.g. Bayesian methods or quasi-experimental approaches) is a plus.  **Programming & Software Engineering**  * Expertise in **Python** (Scala, Kotlin, or Java a plus), with strong **OOP, design patterns, modular architecture, coding standards, version control, testing, and software engineering best practices**.  * **Strong commitment to maintainable, sustainable code**: comfortable refactoring legacy components, raising the engineering bar through reviews, and choosing solutions that age well over short-term fixes that create future pain.  * Experience building **production-ready APIs and services** (e.g. FastAPI), and familiarity with **SQL** and modern data tooling (Pandas/Polars, Spark).  **CI/CD**  * Proficiency in **CI/CD pipelines** for ML models, optimization services, and data pipelines (e.g. **GitHub Actions, GitLab CI/CD**), with version control applied to code, data, and model artifacts.  **MLOps**  * Experience operationalizing ML and decisioning systems with automated workflows for **training, evaluation, deployment, and monitoring** — hands-on with **MLflow** for experiment tracking, model registry, and lifecycle management.  * Knowledge of infrastructure for deploying and scaling models (cloud, containers, Kubernetes); effective collaboration with DevOps and platform teams.  **Data Visualization & APIs**  * Knowledge of tools such as **Plotly, Streamlit**, or similar — and an opinion on what makes a good stakeholder-facing tool.  * Experience designing and consuming **enterprise-level APIs**.  **Generative AI (nice to have)**  * Exposure to **RAG workflows, agentic frameworks, vector databases, prompt engineering, and LLMs**, with interest in applying them to analytics and decisioning use cases.  **Other Skills & Competencies**  * Strong **English** communication; Spanish and/or French are a plus.  * **Analytical, engineering-oriented mindset** with focus on quality, reliability, and reproducibility.  * Effective in **remote, distributed, and multicultural** team environments.  * Able to balance **technical excellence with business deadlines**; proactive, goal-driven, and generous with knowledge.  * Passion for innovation and emerging standards (e.g. **MCP, agentic frameworks, modern optimization tooling**).