Principal Data Scientist

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eSimplicity

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2016

⚕️ Healthcare Insurance

📡 Telecommunications

🤖 Artificial Intelligence

Healthcare Insurance • Telecommunications • Artificial Intelligence

eSimplicity is a company comprised of designers, engineers, and strategists that excels in creating digital services and healthcare IT solutions. They simplify complexity to deliver award-winning products and services that enhance customer experiences, improve public health, and secure the nation. Specializing in areas such as healthcare IT, telecommunications, identity management, and fraud prevention, they work with organizations like the Centers for Medicare & Medicaid Services (CMS) to improve healthcare access and quality. eSimplicity is recognized for their innovative approaches in spectrum management, data analytics, and machine learning, aiming to broaden healthcare coverage and simplify processes for healthcare providers and beneficiaries.

📋 Description

• Lead data science work across structured and unstructured program data, including data collection, processing, cleaning, profiling, and preparation for analysis and modeling on a governed data platform. • Design, develop, train, evaluate, and refine machine learning models and AI services, selecting appropriate algorithms and techniques for specific customer and mission needs. • Support deployment, monitoring, and maintenance of model performance in cloud environments using model lifecycle management, model serving, vector search, model evaluation, and related MLOps tooling. • Deliver capabilities across both program AI tracks: internal AI-enabled delivery acceleration (AI-assisted schema tagging, automated code review, documentation generation, ticket automation) and user-facing AI services for customer users and approved consumers (AI assistants, conversational analytics, document-grounded search, and approved retrieval-augmented generation services). • Operate within the program’s AI governance intake and review process, registering all production and pilot AI use cases before deployment, routing managed endpoints through a governed AI gateway layer, and maintaining inference logging, PII guardrails, rate controls, and human oversight and escalation paths. • Plan and conduct proofs of concept and capability-gate evaluations that assess accuracy, governance integration, cost, operational overhead, and alignment to customer policy before scaling new AI capabilities. • Embed responsible AI and equity requirements into delivery, including algorithmic risk and impact assessments, bias testing across relevant demographic and programmatic subgroups, plain-language limitations and escalation paths, and periodic bias and drift re-review. • Collaborate closely with ML engineers, data engineers, platform teams, and cross-functional partners to develop and maintain the infrastructure and governed cloud environment required for AI/ML operations. • Create, maintain, and improve documentation for methodologies, code, assumptions, experiments, evaluation artifacts, and decisions, including documentation of AI tools within the software bill of materials (SBOM), to support reproducibility and governance review. • Communicate technical findings, strategic vision, risks, tradeoffs, and business value to leadership and key stakeholders, and provide people-management support and day-to-day technical guidance to a team of 3–5 engineers.

🎯 Requirements

• Master’s degree in computer science, data science, statistics, mathematics, or a related field. • 12+ years of experience in data analysis, data modeling, data profiling, and data management, with strong analytical, problem-solving, and critical-thinking skills. • Deep understanding of CMS policies, regulations, and security and privacy expectations, with direct experience in Medicaid, CHIP, or comparable federal data and reporting programs. • All candidates must pass public trust clearance through the U.S. Federal Government. • Strong experience with exploratory data analysis (EDA), feature engineering, analysis, and visualization across structured and unstructured data. • Strong experience with machine learning modeling, including framing business problems, selecting model approaches, training models, evaluating performance, and interpreting results. • Proficiency in at least one programming language or data platform, such as Python, PySpark, R, SQL, Scala, Java, or C++, and experience with common machine learning frameworks and libraries. • Experience with big data technologies, distributed processing, and data science toolsets, and experience using source control and CI/CD pipelines to support version control, collaboration, testing, and repeatable delivery. • Excellent written and verbal communication skills, including the ability to explain complex technical concepts to both technical and non-technical audiences. • Knowledge of sensitive Government data handling, approved data-use practices, least-privilege access, privacy-aware data publication, public data controls, cell suppression, and Section 508/WCAG considerations for public-facing data products. • Ability to comply with customer-specific security, privacy, accessibility, quality, training, and data-handling requirements for assigned systems and data.

🏖️ Benefits

• medical, dental, and vision coverage • 401(k) retirement benefits • paid time off • paid holidays • life and disability insurance • additional wellness and employee support programs