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# Data Scientist within Moderna's Data Science and Artificial Intelligence

- **Company:** NimbusAITech LLC
- **Location:** Cambridge, MA, United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Cloud Computing, Data Mining, Relational Databases, Monitoring of Systems, Internet Communications, Python (Programming Language), PostgreSQL, Machine Learning, Monte Carlo Methods, NumPy, Tensorflow, Software Engineering, Data Streaming, Feature Engineering, Deep Learning, Generative AI, Jupyter, Git, Pandas, Containerization, Scikit Learn, Deployment Automation, Software Version Control, Docker
- **Published:** September 4, 2026
- **Apply:** https://www.dice.com/job-detail/bf98b237-e176-406c-8700-f19dd5916c36

## About the Role

Ph.D. in a quantitative STEM field with 0-2 years of professional experience, OR

Master's degree with 5-8 years of relevant industry experience in data science.

Technical Skills:

Strong fluency in Python and its data science stack (Jupyter, Pandas, NumPy, scikit-learn) along with standard ML frameworks.

Proficiency with relational databases (e.g., PostgreSQL).

Hands-on experience with cloud and deployment tools: AWS, Docker, and Git.

Solid grounding in statistical analysis, feature engineering, and data mining techniques.

Communication & Collaboration: Excellent written, verbal, and remote communication skills in English to collaborate with global, cross-functional teams.

Preferred Qualifications

Experience with mathematical optimization (combinatorial, discrete, convex).

Background or domain knowledge in bioinformatics or scientific/engineering data analysis.

## Description

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 the 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., Model Development & Optimization: Design, train, and tune machine learning models (supervised/unsupervised) and statistical algorithms. Apply techniques such as constrained optimization, combinatorial optimization, and Monte Carlo simulations for schedule optimization and batch generation.

System Monitoring & Anomaly Detection: Implement real-time monitoring of data streams and system logs to identify deviations and flag anomalies.

Data Analysis & Investigation: Analyze large, unstructured, and scientific engineering datasets to determine root causes of flagged anomalies and optimize alert thresholds.

Cross-Functional Collaboration: Partner closely with data engineers, research scientists, statisticians, and product managers to build scalable automated detection pipelines.

Process Enhancement: Explore and integrate emerging Generative AI capabilities to accelerate experimentation and enhance modeling workflows.

Software Best Practices: Implement robust software engineering practices, including version control, containerization, and proper documentation to ensure reproducibility.


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