Data Scientist

Job Title-: 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)

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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 careStrong understanding of business processes and ability to translate data insights into business logicEmphasis 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 productionProficiency in containerization (Docker, etc.)Working knowledge of AWS (specifically SageMaker, pipelines, access management)Understanding of machine learning pipeline orchestrationPreferred Tools/Platforms:AWS ecosystem (SageMaker, Bedrock, etc.)Exposure to LLMs (Large Language Models) or generative AI is a plusNice-to-Have:Background or understanding of insurance or healthcare dataHands-on experience with fraud detection systemsExperience & EducationEducation:Bachelor’s degree acceptable with strong professional experienceMaster’s or PhD preferred but not a hard requirementExperience Level:Approx. 6 years of relevant data science experienceDay-to-Day ResponsibilitiesFocus primarily on model development (core function)Collaborate closely with machine learning engineers for production deploymentEngage in end-to-end data science processes:Data exploration and modelingModel validation and tuningAssisting with model deployment and monitoringWork closely with business stakeholders to understand fraud patterns and operational nuancesTeam StructureReports to Hiring ManagerCollaborates 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 InsightsPreference 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. xzbcwsv 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