Moderna Data Scientist Jobs (NOW HIRING)

Joining Moderna means advancing mRNA science to transform medicine. Work with exceptional global teams on a broad pipeline and build a career that makes a real difference for patients. Moderna is strengthening its international business services hub in Warsaw, supporting our growing global operations.

We welcome professionals ready to help advance our mission and shape the future of mRNA medicines. As a Data Scientist within Moderna’s Data Science and Artificial Intelligence (DSAI) team, you will design and deploy advanced machine learning and optimization solutions that power our manufacturing and technical development ecosystems. You will play a critical role in scaling the production of mRNA therapeutics, transforming complex data into actionable insights that directly impact patients worldwide.

This is a high-impact individual contributor role where scientific curiosity, computational rigor, and operational awareness converge to accelerate innovation across Moderna’s platform. Here’s What You’ll Do: • Support a wide variety of analytical, quality control, and manufacturing processes through advanced data analysis and visualization, statistical modeling, and Bayesian experimental design • Apply advanced techniques such as constrained optimization, machine learning, and Monte Carlo simulations to solve complex challenges including schedule optimization and batch generation • Identify high-impact opportunities by leveraging and applying the latest advances in computer science and operations research, continuously staying at the forefront of the field • Partner closely with cross-functional business and product stakeholders to iteratively align on project goals across the full lifecycle—spanning data acquisition, modeling strategy, validation, deployment, and monitoring • Collaborate deeply with data scientists, engineers, research scientists, statisticians, and manufacturing teams to drive integrated, scalable solutions • Champion and implement data science and software engineering best practices to ensure robustness, reproducibility, and scalability of solutions • Communicate complex analytical findings clearly and effectively to both technical and non-technical audiences, internally and externally • Explore and integrate emerging Generative AI capabilities to enhance modeling approaches, accelerate experimentation, and unlock new efficiencies across manufacturing and development workflows Here’s What You’ll Need (Basic Qualifications) • Ph.D. in a quantitative STEM field (technology, engineering, and mathematics) with 0-2 years of professional experience, or a Master''s degree plus • 5-8 years of relevant professional experience required.

• Experience with optimization (combinatorial, discrete, convex, etc.) preferred but not required. • Background in bioinformatics preferred but not required. • Experience delivering data science projects analyzing and modeling scientific engineering data, preferably in an industry setting.

• Outstanding communication skills (verbal, written and remote). • Demonstrated experience in collecting, cleaning, and analyzing large and/or unstructured datasets and effectively communicating insights. • Fluency in Python, especially the data scientific stack (Jupyter/Pandas/scikit-learn) and machine learning libraries • Familiarity with best practices in software development, including Amazon Web Services, Docker, version control (Git), and documentation.

• Working knowledge of relational databases (e.g., PostgreSQL). • Ability to manage multiple projects and effectively collaborate in a dynamic, cross-functional environment. • Proficiency in English (verbal and/or written) required due to global collaboration needs Key Responsibilities • Model Development: Design, train, and tune machine learning models (unsupervised/supervised) and statistical algorithms to detect anomalies.

• System Monitoring: Implement real-time monitoring of data streams and system logs to identify deviations from expected behavior. • Data Analysis & Investigation: Analyze large, complex datasets to investigate root causes of flagged anomalies. • Alert Optimization: Reduce false positives by tuning detection thresholds, ensuring high-accuracy alerts.

• Collaboration: Work with product management, data engineers and IT teams to implement data quality, security, and automated detection pipelines • Data Techniques: Strong understanding of statistical analysis, data mining, and feature engineering.