Data Scientist

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.