Quest Analytics - Senior Data Scientist
Requirements
• 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 • 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
Responsibilities
• 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 • 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 • 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
Benefits
• Competitive salary and success sharing bonus • Flexible vacation, plus sick time and holidays • An entrepreneurial culture that won’t limit you to a job description • Being listened to, valued, appreciated -- and having your contributions rewarded • Enjoying your work each day with a great group of people • careers.questanalytics.com
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