Data Scientist

The team is seeking a Data Scientist with strong MLOps and full-stack data experience, capable of both developing models and supporting production deployment within an AWS environment.

Key Focus Areas:

• Fraud, Waste, and Abuse (FWA) detection in long-term care
• Strong understanding of business processes and ability to translate data insights into business logic
• Emphasis on candidates who can learn and adapt to new business contexts, Day-to-Day Responsibilities
• Focus primarily on model development (core function)
• Collaborate closely with machine learning engineers for production deployment
• Engage in end-to-end data science processes:
• Data exploration and modeling
• Model validation and tuning
• Assisting with model deployment and monitoring
• Work closely with business stakeholders to understand fraud patterns and operational nuances

Team Structure

• Reports to Marin (Hiring Manager)
• Collaborates with:
• Senior Data Scientists (peer mentors and project leads)
• Machine Learning Engineers (for deployment/productionization)
• Business partners (for domain understanding and data interpretation)

Interview Process

Three rounds total:

1. Technical Assignment & Presentation + Candidate receives a small project beforehand + Expected to present findings during interview
2. Technical Interview + Deep-dive discussion around project and technical skills
3. Final Interview + With Marin and other team members + Focus on team fit, communication, and business understanding

• Programming: Python (required)
• MLOps / Full-Stack Data Science:
• Experience in deploying machine learning models to production
• Proficiency in containerization (Docker, etc.)
• Working knowledge of AWS (specifically SageMaker, pipelines, access management)
• Understanding of machine learning pipeline orchestration

Preferred Tools/Platforms:

• AWS ecosystem (SageMaker, Bedrock, etc.)
• Exposure to LLMs (Large Language Models) or generative AI is a plus

Nice-to-Have:

• Background or understanding of insurance or healthcare data
• Hands-on experience with fraud detection systems, + Bachelor’s degree acceptable with strong professional experience
• Master’s or PhD preferred but not a hard requirement
• Experience Level:
• Approx. 3 years of relevant data science experience (Level 1 Data Scientist), * Preference for candidates who are strong in MLOps even if slightly less advanced in pure data science theory.
• The ability to grasp new business models quickly is critical, especially for FWA detection.
• Ideal candidate demonstrates hands-on experience with both model building and AWS-based deployment.
• Someone with experience using large language models or recent GenAI technologies would stand out.

Experience Level: 3 years (Level 1 Data Scientist)

Environment/Tools: AWS (SageMaker, Bedrock, etc.)

Programming Languages: Python required