Senior Data Scientist

At Quest Analytics, the models and insights our Data Science team delivers help solve real problems around the accuracy, adequacy, and accessibility of healthcare provider networks. We're looking for a Senior Data Scientist to join our team. In this role, you'll leverage complex healthcare data to drive insights, improve data quality, and enable product innovation. You'll develop scalable machine learning models and AI-driven approaches to solve high-impact business problems for health plan networks, and help shape how Quest Analytics continues to evolve its data science capabilities. This is a senior individual contributor role with significant opportunity to influence our products, technical approaches, and data strategy, including how Quest Analytics uses machine learning, advanced analytics, and emerging AI technologies. If you're someone who likes digging into complicated datasets, challenging assumptions, experimenting with new approaches, and ultimately turning that work into scalable solutions, we'd love to talk with you.

You'll partner across Data, Product, Engineering, and Client teams to: Design, develop, optimize, and scale machine learning models and advanced analytics solutions to improve provider data quality and generate meaningful network insights Apply statistical modeling, predictive analytics, and machine learning techniques (classification, regression, clustering, NLP, anomaly detection) to solve business problems Research open-ended healthcare and provider-data challenges, form hypotheses, and develop action plans and analytical approaches to turn findings into actionable solutions Develop novel algorithms to solve complex or unprecedented problems Evaluate and improve existing models and algorithms to increase accuracy, performance, scalability, and reliability, and measure the resulting business impact Perform large-scale data processing and analysis using Python, SQL, Databricks, Spark, and distributed computing across structured and unstructured datasets Develop and maintain automated systems for anomaly detection, data validation, feature engineering, and continuous model monitoring Design feature engineering strategies and evaluate model performance using appropriate validation techniques Evaluate and integrate third-party data sources, tools, and vendors to strengthen our datasets and analytical capabilities Partner with Engineering to productionize models and build scalable, reliable solutions within our software products Apply LLM tools such as Claude to accelerate analysis, automate workflows, and improve documentation, and explore opportunities in generative AI, NLP, and AI agents Develop and maintain metrics to evaluate model performance, data quality, and business impact Translate complex findings into clear, actionable insights for technical and non-technical stakeholders Present findings and recommendations to Network Analytics leadership and broader company audiences Partner with Product and business teams to identify opportunities for new features, metrics, and data-driven solutions Mentor other data scientists and analysts, and contribute to technical standards, data governance, and MLOps best practices Participate in cross-functional initiatives and support client-facing analytics discussions as needed

7+ years of experience in data science, machine learning, or advanced analytics, preferably in a SaaS organization Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related discipline, or equivalent relevant experience Demonstrated experience building and delivering machine learning models into production Advanced Python skills, including tools such as pandas, NumPy, and scikit-learn Strong experience with Databricks, Spark, or comparable distributed computing technologies Advanced SQL skills Experience with ML frameworks such as TensorFlow, PyTorch, or similar technologies Strong understanding of machine learning techniques including classification, regression, clustering, NLP, and anomaly detection Strong applied statistics knowledge, including hypothesis testing, regression analysis, and probability Experience working with large, messy, and complex datasets Understanding of MLOps, including deployment, monitoring, validation, and model lifecycle management Experience using LLMs and AI-assisted tools to improve data science workflows Ability to independently tackle ambiguous problems and determine an effective path forward Ability to explain complex technical findings in a way that creates clear business value Strong collaboration skills and experience working across Product, Engineering, Data, and business stakeholders Experience leading technical initiatives and mentoring others Experience in one or more of these areas would be especially valuable: Healthcare provider data, provider networks, or healthcare analytics Healthcare, SaaS, or another highly regulated data environment Integrating ML models into production software products Data quality, entity resolution, anomaly detection, or large-scale data validation Generative AI, NLP, LLM applications, or agentic workflows Data visualization and communicating analytical results to clients or executive audiences

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