{"id":1090581,"url":"https://alion.io/job/quest-analytics-llc-senior-data-scientist","title":"Senior Data Scientist","company":{"id":52094,"name":"Quest Analytics Llc","domain":"questanalytics.com","url":"https://alion.io/company/quest-analytics-llc","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":116000,"max_usd":223000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1089},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Claude","optional":false},{"name":"Databricks","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-09-14T17:11:24Z","employer_posted_date":"2026-09-14","last_verified_at":"2026-10-13T03:30:12Z","board_verified":true,"closed_at":null,"days_open":28,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":28},"description":"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.\nWe'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.\nThis 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.\nIf 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.\nWhat You'll Do:\nYou'll partner across Data, Product, Engineering, and Client teams to:\nDesign, develop, optimize, and scale machine learning models and advanced analytics solutions to improve provider data quality and generate meaningful network insights\nApply statistical modeling, predictive analytics, and machine learning techniques (classification, regression, clustering, NLP, anomaly detection) to solve business problems\nResearch open-ended healthcare and provider-data challenges, form hypotheses, and develop action plans and analytical approaches to turn findings into actionable solutions\nDevelop novel algorithms to solve complex or unprecedented problems\nEvaluate and improve existing models and algorithms to increase accuracy, performance, scalability, and reliability, and measure the resulting business impact\nPerform large-scale data processing and analysis using Python, SQL, Databricks, Spark, and distributed computing across structured and unstructured datasets\nDevelop and maintain automated systems for anomaly detection, data validation, feature engineering, and continuous model monitoring\nDesign feature engineering strategies and evaluate model performance using appropriate validation techniques\nEvaluate and integrate third-party data sources, tools, and vendors to strengthen our datasets and analytical capabilities\nPartner with Engineering to productionize models and build scalable, reliable solutions within our software products\nApply LLM tools such as Claude to accelerate analysis, automate workflows, and improve documentation, and explore opportunities in generative AI, NLP, and AI agents\nDevelop and maintain metrics to evaluate model performance, data quality, and business impact\nTranslate complex findings into clear, actionable insights for technical and non-technical stakeholders\nPresent findings and recommendations to Network Analytics leadership and broader company audiences\nPartner with Product and business teams to identify opportunities for new features, metrics, and data-driven solutions\nMentor other data scientists and analysts, and contribute to technical standards, data governance, and MLOps best practices\nParticipate in cross-functional initiatives and support client-facing analytics discussions as needed\nWhat You'll Need:\n7+ years of experience in data science, machine learning, or advanced analytics, preferably in a SaaS organization\nMaster's degree in Data Science, Computer Science, Statistics, Mathematics, or a related discipline, or equivalent relevant experience\nDemonstrated experience building and delivering machine learning models into production\nAdvanced Python skills, including tools such as pandas, NumPy, and scikit-learn\nStrong experience with Databricks, Spark, or comparable distributed computing technologies\nAdvanced SQL skills\nExperience with ML frameworks such as TensorFlow, PyTorch, or similar technologies\nStrong understanding of machine learning techniques including classification, regression, clustering, NLP, and anomaly detection\nStrong applied statistics knowledge, including hypothesis testing, regression analysis, and probability\nExperience working with large, messy, and complex datasets\nUnderstanding of MLOps, including deployment, monitoring, validation, and model lifecycle management\nExperience using LLMs and AI-assisted tools to improve data science workflows\nAbility to independently tackle ambiguous problems and determine an effective path forward\nAbility to explain complex technical findings in a way that creates clear business value\nStrong collaboration skills and experience working across Product, Engineering, Data, and business stakeholders\nExperience leading technical initiatives and mentoring others\nExperience in one or more of these areas would be especially valuable:\nHealthcare provider data, provider networks, or healthcare analytics\nHealthcare, SaaS, or another highly regulated data environment\nIntegrating ML models into production software products\nData quality, entity resolution, anomaly detection, or large-scale data validation\nGenerative AI, NLP, LLM applications, or agentic workflows\nData visualization and communicating analytical results to clients or executive 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