Senior Data Scientist - ML Engineering & MLOps

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 a Senior Data Scientist - ML Engineering, you will join the Global Analytics and Technology Center of Excellence (GATE) in San José, Costa Rica. In this role, you will own the end-to-end path from machine learning models to scalable, reliable production systems supporting global Roche affiliates. Operating across the full MLOps and software engineering stack, you will architect cloud-native ML pipelines, build automated testing and CI/CD frameworks, instrument model observability, and set high software craftsmanship standards across the engineering team.

• ML Systems & Platform Engineering: Architect, deploy, and maintain end-to-end production ML services, training pipelines, containerization, and orchestration (Airflow/Dagster) with strict SLAs for performance and cost.
• MLOps Capability Building: Design reusable MLOps platform components including feature stores, model registries, experiment tracking, and automated CI/CD deployment pipelines using GitLab CI or GitHub Actions.
• Model Accuracy & Observability: Build automated back-testing frameworks and instrumentation for tracking data/concept drift, feature anomalies, and model performance decay, translating diagnostic findings into root-cause fixes.
• Cloud & Pipeline Automation: Implement cloud-native ML infrastructure across AWS, Azure, or GCP, systematically automating manual tasks and maintaining data synchronization contracts.
• Technical Leadership & Coaching: Establish engineering standards through code reviews, refactoring research code, and mentoring data scientists on production-grade software practices.
• Stakeholder Support: Partner with global affiliate teams to explain predictive model outputs, address business needs, and incorporate feedback into technical roadmaps.

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Statistics, or a quantitative discipline.
• 7+ years of relevant experience running and maintaining machine learning systems in production environments.
• Deep proficiency in Python, Linux/Bash, SQL, modern pipeline tools (Spark, dbt, Snowflake, Databricks), and clean API design.
• Hands-on expertise with containerization (Docker, Kubernetes), MLOps tools (MLflow, Weights & Biases), orchestration (Airflow), and CI/CD automation.
• Proven experience in cloud architectures (AWS, Azure, or GCP) and core ML frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow, time-series forecasting).
• Strong analytical mindset focused on quality control, data contracts, and system observability (experience with LLMOps, streaming/Kafka, or GPU workloads is a plus).
• Must be currently based in Costa Rica or hold valid work authorization for San José.

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.