ONLY LOCAL || W2 || Data Scientist at Cambridge, MA

Position : Data Scientist

Location : Cambridge, MA Local

• 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.