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).