Senior Data Scientist, Costing

• Use data science and machine learning principles to develop effective predictive models
• Write software to prepare, clean, and sample data for use in developing predictive models
• Use cloud resources (e.g., Amazon Web Services) to prepare and process data
• Query and extract data from databases (Snowflake)
• Use data analysis and visualization tools (examples include SQL, Python, Jupyter Notebooks, and Looker) to inform the business strategy
• Relentlessly iterate solutions within a fast-paced environment where ambiguity is the norm
• Solve challenging, uncharted problems
• Work in an environment that thrives on teamwork and continuous learning opportunities

• Bachelor's degree required; degree in applied math, computer science, natural sciences or engineering preferred
• M.S. or Ph.D. in a related field highly desired
• 5+ years of experience with machine learning, statistical modeling, and optimization techniques
• Fluent in Python (pandas, numpy, SciPy, and scikit-learn preferred)
• Proficient in linear algebra and statistics
• Familiar with scientific software principals, e.g. versioning systems, reproducibility
• Experience in the manufacturing industry is desired
• Must be a US Citizen or Green Card holder (ITAR)

The estimated base salary range for new hires into this role is $140,000-$182,000 annually (+ commission OR + annual bonus) depending on factors such as job-related skills, relevant experience, and location. We also offer a competitive benefits package, including 401(k) match, medical, dental and vision insurance; life and disability insurance; generous paid time off including vacation, sick leave, floating and fixed holidays, maternity and bonding leave; EAP, other wellbeing resources; and much more.

Xometry (NASDAQ: XMTR) powers the industries of today and tomorrow by connecting the people with big ideas to the manufacturers who can bring them to life. Xometry's digital marketplace gives manufacturers the critical resources they need to grow their business while also making it easy for buyers at Fortune 1000 companies to tap into global manufacturing capacity.