Associate Data Scientist

About the Role

The Home Data Science team is seeking an Associate Data Scientist to join its applied ML practice. This role offers hands-on work, direct feedback, and investment in development. The position requires a hybrid schedule, based at the South Charlotte, NC Headquarters (Tuesday through Thursday) with remote work on Mondays and Fridays.

Key Responsibilities

• Contribute to the development and deployment of ML/AI solutions in production environments, focusing on reliable, well-tested code.
• Manage the full data lifecycle: pulling and cleaning data, building models, and tracking downstream impact.
• Customize solutions from platform teams for individual clients and partners.
• Collaborate with data scientists, engineers, and business stakeholders to translate problems into data science work.
• Clearly communicate progress, risks, and results to technical and non-technical audiences.
• Participate in code reviews and pair programming sessions.
• Build working knowledge of key business KPIs and how model outputs connect to them.
• Independently take on scoped tickets and small projects, escalating blockers early.
• Develop proficiency in common ML techniques, model deployment practices, and team tools/platforms.

Requirements & Tech Stack

• Python
• SQL
• Experience with Generative AI tools such as Claude Code, Codex, Cursor, Gemini, Langchain, Mastra, Open AI or similar.
• Familiarity with model deployment and ML operations concepts, including version control, CI/CD, and job orchestration.
• Analytical instincts for exploratory data analysis.
• Ability to assess technical model performance and its business impact.
• Collaborative working style with curiosity about business drivers.
• Databricks or cloud platforms (AWS) is a bonus.

Qualifications

• Master's degree in a quantitative field (Computer Science, Statistics, Economics, Mathematics, or similar).

OR

• Bachelor's degree with 1-2 years of professional experience.
• Foundational understanding of core data science techniques such as classification, regression, and clustering, and when to apply them.
• Prior internship, research, or project experience applying ML to real-world problems is a strong plus.