Manager - Data Science, Tech Lead

November 18

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Xometry

WebsiteLinkedInAll Job Openings

Direct Metal Laser Sintering (DMLS) • CNC Machining • Production Manufacturing • Rapid Prototyping • 3D Printing

1001 - 5000 employees

Founded 2013

💰 $75M Series E on 2020-09

Description

• Lead and manage a team of data scientists, providing technical direction, mentorship, and guidance to support their professional growth and ensure high-quality output. • Use data analysis, visualization, and modeling tools (e.g., Looker, Jupyter Notebooks, Python, Pandas) to drive data-driven business strategies, with a focus on cost prediction for a two-sided marketplace. • Design and execute data science projects, including hypothesis development, statistical analysis, and experimentation (e.g., A/B testing) to answer key business questions and validate assumptions. • Collaborate with cross-functional teams to translate business needs into data science solutions and ensure alignment with Xometry’s objectives. • Analyze existing algorithms for accuracy and performance, provide recommendations for optimization, and update production code as necessary. • Query and extract data from databases (e.g., Snowflake, MongoDB) to support analysis and model development. • Implement robust data science methodologies, emphasizing best practices in code reproducibility, version control, and model deployment. • Continuously iterate and improve solutions within a fast-paced, agile environment, addressing challenges with innovative, data-driven approaches. • Foster a culture of collaboration, teamwork, and continuous learning, encouraging knowledge sharing and skill development within the team.

Requirements

• A bachelor’s degree is required; an advanced degree (M.S. or PhD) in applied math, computer science, data science, engineering, or a related field is highly preferred. • 7+ years of experience in data science, with at least 3 years in a leadership role (managing data science projects or teams). • Strong expertise in SQL and Python (numpy, scipy, pandas, and scikit-learn preferred) and experience with API integrations using Python. • Proficient in applied statistics, including knowledge of probability distributions, confidence intervals, statistical tests (e.g., t-tests, ANOVA, Chi-Square), and power analysis. • Experience with pricing algorithms and technology-driven product development. • Knowledge of scientific software principles, including version control, code reproducibility, and deployment practices. • Demonstrated ability to work in a fast-paced, ambiguous environment and manage shifting priorities effectively. • Must be able to work core hours aligned with US Eastern Time (GMT-5). • Strong communication and interpersonal skills to collaborate with global teams and articulate complex technical information to non-technical stakeholders.