Data Scientist

Job Titile-: Data Scientist
Duration : 6-month contract with potential for full-time conversion
Location -: Newark, NJ (Hybrid)
Department : Data Science Team - Long-Term Care (Fraud, Waste, and Abuse Detection)

Descritption-:
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

Technical Requirements
Core Skills:

• 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

Experience & Education

• Education :
  • 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)

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 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

Key Insights

• 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