6. Key Responsibilities
As a Senior Data Scientist on the Costing team, your primary mandate is to improve the accuracy and efficiency of price prediction. You will be responsible for the full lifecycle of your models: extracting data from Snowflake, cleaning and sampling it using Python, and deploying it via cloud resources like AWS.
You will collaborate closely with product managers and engineering teams to integrate these models into the Xometry marketplace. Beyond coding, you are expected to be a force for continuous learning, often tasked with solving "uncharted problems" where the path to a solution is not pre-defined.
7. Role Requirements & Qualifications
Xometry looks for candidates who are not only technically proficient but also capable of operating in a fast-paced, iterative environment.
- Must-have skills: 5+ years of experience with machine learning and statistical modeling; fluency in Python (pandas, numpy, scikit-learn); strong SQL proficiency; and a solid grasp of linear algebra.
- Nice-to-have skills: Experience within the manufacturing industry; a Ph.D. or M.S. in a related field; and hands-on experience with cloud infrastructure, specifically AWS.
- Soft skills: The ability to thrive in ambiguity and a proactive approach to teamwork.
8. Frequently Asked Questions
Q: How can I prepare for the "intense" math questions?
A: Review core statistical concepts and linear algebra fundamentals. Focus on the "why" and "how" of algorithms rather than just their implementation.
Q: Is the interview process strictly technical?
A: While technical depth is the priority, ensure you can communicate the business impact of your work. The team looks for candidates who understand how their models drive the bottom line.
Q: What is the typical timeline for the process?
A: While it varies, candidates should expect a few weeks from the initial screen to the final round. Stay communicative with your recruiter.
Q: How should I handle the ambiguity of the role?
A: In your interviews, demonstrate how you break down large, ill-defined problems into actionable, measurable steps.
9. Other General Tips
- Own your time: If an interviewer is late or a session is rescheduled, remain professional and composed. Your conduct under pressure is part of the evaluation.
- Prepare for live coding: Practice writing clean, efficient Python code in a sandbox environment without autocomplete tools.
- Relate to manufacturing: Even if you haven't worked in the industry, research how digital marketplaces function and how predictive models impact supply chain logistics.