Data Scientist

Job Description

This position is not eligible for sponsorship.

Day-to-Day Responsibilities

• Build robust Agentic AI and RAG applications using Responsible AI practices across model development, deployment, and governance
• Participate in the end-to-end data science project lifecycle - data mining and exploration, model development and evaluation, production deployment, measurement, and tracking
• Perform time-series analyses, hypothesis testing, and causal analyses to statistically assess impact and extract trends across functional areas
• Build statistical models to enhance understanding of trends and predict future performance
• Design experiments and interpret results to draw detailed, actionable conclusions
• Design, validate, and evaluate solutions using Python, SQL, and other programming tools
• Transform data into actionable insights and recommendations, and support standard analyses, reports, and dashboards
• Collaborate with stakeholders and other teams to gather data, build relationships, and champion ML capabilities for non-technical audiences
• Educate and mentor team members on data science best practices, statistical programming, and data preparation

Minimum Requirements

• 5 years of experience as a Data Scientist working with large databases to perform complex analysis
• Master's degree in a STEM field
• Strong MLOps background - big focus, including carrying models through the full development cycle into live production
• 5 years of Python
• 5 years of SQL
• Git experience
• Experience building and deploying ML/AI models in a cloud computing environment (e.g., AWS) and/or enterprise IT environment
• Strong statistical modeling skills (multivariate regression, logistic regression, cluster analysis, design of experiments, decision trees)
• Strong communication skills and experience working directly with stakeholders
• Hands-on AI experience (at minimum, active personal use)

Preferred Qualifications

• Snowflake
• AWS
• Startup mindset
• Proficiency with Excel and PowerBI
• Experience with commercial analytics (pricing optimization, customer segmentation, churn/LTV) or operational analytics (logistics, route optimization, maintenance optimization)