Data Scientist in Newark

Job Description

Job Title-: Data Scientist

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Duration: 6-month contract with potential for full-time conversion

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Location-: Newark, NJ (Hybrid)

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

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Description-:

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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.

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Key Focus Areas:

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• Fraud, Waste, and Abuse (FWA) detection in long-term care

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• Strong understanding of business processes and ability to translate data insights into business logic

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• Emphasis on candidates who can learn and adapt to new business contexts

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Technical Requirements

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Core Skills:

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• Programming: Python (required)

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• MLOps / Full-Stack Data Science:

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• Experience in deploying machine learning models to production

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• Proficiency in containerization (Docker, etc.)

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• Working knowledge of AWS (specifically SageMaker, pipelines, access management)

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• Understanding of machine learning pipeline orchestration

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Tools/Platforms:

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• AWS ecosystem (SageMaker, Bedrock, etc.)

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• Exposure to LLMs (Large Models) or generative AI is a plus

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Nice-to-Have:

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• Background or understanding of insurance or healthcare data

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• Hands-on experience with fraud detection systems

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Experience & Education

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• Education:

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• Bachelor’s degree acceptable with strong professional experience

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• Master’s or PhD but not a hard requirement

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• Experience Level:

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• Approx. 6 years of relevant data science experience

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Day-to-Day Responsibilities

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• Focus primarily on model development (core function)

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• Collaborate closely with machine learning engineers for production deployment

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• Engage in end-to-end data science processes:

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• Data exploration and modeling

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• Model validation and tuning

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• Assisting with model deployment and monitoring

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• Work closely with business stakeholders to understand fraud patterns and operational nuances

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Team Structure

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• Reports to Hiring Manager

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• Collaborates with:

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• Senior Data Scientists (peer mentors and project leads)

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• Machine Learning Engineers (for deployment/product ionization)

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• Business partners (for domain understanding and data interpretation)

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Key Insights

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• Preference for candidates who are strong in MLOps even if slightly less advanced in pure data science theory.

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• The ability to grasp new business models quickly is critical, especially for FWA detection.

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• Ideal candidate demonstrates hands-on experience with both model building and AWS-based deployment.

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• Someone with experience using large models or recent GenAI technologies would stand out.

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

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• Programming : Python required

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