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Sabana Norte, San Jose, Costa Rica
Actively hiring, first seen by us 11 hours agoEmployment type in the employer’s posting: Regular / PermanentFrom the employer’s posting
You will own the technical direction and delivery of both.
Lead, mentor and develop Senior Data Scientists and Data Scientists across both sub-streams.
You will work directly with product owners to turn business questions into predictive, causal and optimization solutions, and you will grow a team of about 7 data scientists.
The Position As Engagement Lead, you will lead two data science sub-streams supporting commercial teams: Predictive AI & Optimization and Causal AI & Experimentation. You will own the technical direction and delivery of both. You will work directly with product owners to turn business questions into predictive, causal and optimization solutions, and you will grow a team of about 7 data scientists. This is a hands-on leadership role. You will review models and code, unblock the team quickly, and be accountable for the quality and business impact of everything the two sub-streams deliver. Key Responsibilities
Key Responsibilities Lead, mentor and develop Senior Data Scientists and Data Scientists across both sub-streams. Build technical depth, modular coding standards and agile delivery habits. Lead the design, calibration and validation of Bayesian and frequentist Marketing Mix Models (MMM). Models should account for adstock, saturation, seasonality and hierarchical or geo-level effects.
10+ years of experience in Data Science/Analytics, including at least 3 years leading data science teams and mentoring data scientists. You should have a proven track record of measurable business impact through predictive modelling and causal inference or experimentation, ideally in commercial or marketing analytics.
Deep expertise in causal inference and experimentation, including experimental design, A/B testing, quasi-experimental methods and econometrics.
Hands-on experience building Marketing Mix Models and multi-touch attribution models (regression-based, time-decay, Markov-chain and Shapley-value methods), using frameworks such as Google Meridian, PyMC-Marketing, Meta Robyn, LightweightMMM, Orbit, PyMC or Stan. This includes model diagnostics (convergence, priors, posterior predictive checks) and turning outputs into budget optimization, response curves and scenario simulations.
Education & Certification: - Educational Background: Bachelor's or Master's degree in Data Science, Statistics, Economics, Artificial Intelligence and Machine Learning, Computer Science, Business Analytics or another quantitative field. - Certifications: Recognized professional certifications in Data Science, Machine Learning, Cloud AI Platforms or Advanced Analytics are an advantage.
A healthier future drives us to innovate.
In the employer’s words · Read in context
Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
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- Hands-on experience building Marketing Mix Models and multi-touch attribution models (regression-based, time-decay, Markov-chain and Shapley-value methods), using frameworks such as Google Meridian, PyMC-Marketing, Meta Robyn, LightweightMMM, Orbit, PyMC or Stan. This includes model diagnostics (convergence, priors, posterior predictive checks) and turning outputs into budget optimization, response curves and scenario simulations. - Expert proficiency in Python and SQL, with strong experience in Spark (distributed computing), Airflow, Git, AWS, and Tableau or an equivalent tool for visualization and storytelling. - Exposure to and recent experience with GenAI tools and LLMs (e.g., OpenAI, Claude, Gemini, DeepSeek) and coding assistants such as GitHub Copilot, plus experience of building AI agents using frameworks such as LangChain, LangGraph, AutoGen or CrewAI.
- 10+ years of experience in Data Science/Analytics, including at least 3 years leading data science teams and mentoring data scientists. You should have a proven track record of measurable business impact through predictive modelling and causal inference or experimentation, ideally in commercial or marketing analytics. - Strong hands-on expertise in predictive modelling, including classification on skewed data with imbalance handling (e.g., SMOTE, class weighting), ensemble and tree-based models (LightGBM, XGBoost, CatBoost), linear models, anomaly detection, unsupervised methods (clustering, segmentation) and time-series forecasting (e.g., ARIMA, Prophet). - Rigorous model validation and explainability, including SHAP and precision-recall and calibration methods for imbalanced problems.
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New Data Scientist postings at the employers we follow are down 4% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.
As Engagement Lead, you will lead two data science sub-streams supporting commercial teams: Predictive AI & Optimization and Causal AI & Experimentation. You will own the technical direction and delivery of both. You will work directly with product owners to turn business questions into predictive, causal and optimization solutions, and you will grow a team of about 7 data scientists. This is a hands-on leadership role. You will review models and code, unblock the team quickly, and be accountable for the quality and business impact of everything the two sub-streams deliver.
Key Responsibilities
Who You Are
You are an accomplished data science leader with deep expertise in both predictive modelling and causal inference. You know when a problem needs a prediction, when it needs a causal estimate, and when it needs an optimization layer on top. You lead by example, stay close to the code, and can explain complex model outputs, from an MMM posterior to a SHAP plot, in clear business terms that product owners can act on.
Education & Certification:
Qualifications & Technical Skillset:
This position does not offer relocation benefits.
A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.
Let’s build a healthier future, together.
Roche is an Equal Opportunity Employer.
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Sabana Norte, San Jose, Costa Rica
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