Data Scientist Job at SPECTRAFORCE in Newark

Department: Data Science Team – Long-Term Care (Fraud, Waste, and Abuse Detection)

Description: -: 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. 6 years of relevant data science experience 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/product ionization) Business partners (for domain understanding and data interpretation) 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.

Environment/Tools: AWS (SageMaker, Bedrock, etc.) Programming Languages: Python required