Machine Learning Engineer (Data Scientist)
Bold Metrics is a San Francisco-based company providing AI-powered SaaS solutions for body data, apparel fit, sizing, and eCommerce personalization. Its technology helps brands improve conversion and order value while reducing fit-related returns.
Funding history
About Bold Metrics, Inc.
Bold Metrics provides an AI body data platform that uses digital twin technology and machine learning to derive detailed body measurements from simple survey inputs. Its solutions support personalized size recommendations, virtual fitting, apparel design and merchandising insights, returns reduction, and agentic commerce. The company serves apparel brands, retailers, custom clothiers, tailors, and other organizations seeking body-data-driven customer experiences and operational insights.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You are a humble, passionate, and experienced engineer looking for a collaborative and analytical Data Scientist role to support product deliveries and the machine learning pipeline. You will validate data flows, analyze patterns and trends, facilitate machine learning updates, and work with Engineering, Data, and Customer Success teams.
Requirements
- BSc or MSc in Mathematics, Computer Science, Engineering, or a related field.
- Over 3 years of hands-on experience in Data Science, Machine Learning, and Deep Learning.
- 4+ years of demonstrated high proficiency in Python and SQL.
- Practical expertise in A/B testing and causal inference.
- Extensive experience with AWS cloud technologies.
- Proficiency in MLOps practices, including model deployment and monitoring.
- Experience with Docker for containerization and Git for version control.
Responsibilities
- Lead strategic decision-making for user growth and product innovation using analytics, experimentation, forecasting, and machine learning.
- Develop and deploy machine learning models and causal inference techniques at scale, from concept to measurable business impact.
- Design and oversee experiments and analyses to evaluate product performance and drive continuous improvements.
- Build and maintain automated, scalable data pipelines supporting reporting, data visualization, analytics, and machine learning.
- Lead the design and management of comprehensive machine learning platforms and robust model development and deployment processes.