FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Senior Data Scientist
Quest Analytics, LLC. Design, develop, optimize, and scale machine learning models and advanced analytics solutions for provider data quality and network insights .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Expertise in designing and developing machine learning models and advanced analytics solutions, with a strong focus on data quality and network insights. Proficient in statistical modeling, predictive analytics, and MLOps practices to drive business impact and deliver actionable insights.
Highest-signal resume keywords
Machine Learning Model DevelopmentAdvanced Python SkillsExperience with Databricks and SparkApplied Statistics KnowledgeMLOps Understanding
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningStatistical ModelingPredictive AnalyticsFeature EngineeringAnomaly DetectionClassificationRegressionClusteringNLPData Processing
Soft Skills
CollaborationMentoringProblem SolvingCommunication
Tools & Technologies
PythonSQLDatabricksSparkTensorFlowPyTorchLLM ToolsAI Agents
Certifications & Qualifications
Master's Degree in Data ScienceMaster's Degree in Computer ScienceMaster's Degree in StatisticsMaster's Degree in Mathematics
Industry Keywords
Healthcare AnalyticsData QualitySaaSData GovernanceMLOps
Tech Stack
Tools & technologiesNumpyPandasPythonPyTorchScikit-LearnSparkSQLTensorflow
About the role
Key responsibilities & impact- Design, develop, optimize, and scale machine learning models and advanced analytics solutions for provider data quality and network insights
- Apply statistical modeling, predictive analytics, and machine learning techniques to solve business problems
- Research healthcare and provider-data challenges, form hypotheses, and develop analytical approaches
- Develop novel algorithms for complex problems
- Evaluate and improve model accuracy, performance, scalability, reliability, and business impact
- Perform large-scale data processing and analysis using Python, SQL, Databricks, Spark, and distributed computing
- Develop and maintain automated systems for anomaly detection, data validation, feature engineering, and continuous model monitoring
- Design feature engineering strategies and evaluate model performance
- Evaluate and integrate third-party data sources, tools, and vendors
- Partner with Engineering to productionize models and build scalable software solutions
- Apply LLM tools such as Claude and explore generative AI, NLP, and AI agents
- Develop metrics for model performance, data quality, and business impact
- Translate complex findings into actionable insights
- Present findings and recommendations to leadership and company audiences
- Partner with Product and business teams on features, metrics, and data-driven solutions
- Mentor data scientists and analysts; contribute to technical standards, data governance, and MLOps best practices
- Participate in cross-functional initiatives and client-facing analytics discussions
Requirements
What you’ll need- 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 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 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
- Applicants must be legally authorized to work in the United States
- Visa sponsorship is not available at this time
- Preferred work locations are within one of the listed United States states
Benefits
Comp & perks- Workplace flexibility – choose between remote, hybrid or in-office
- Affordable employee medical, dental and vision plans with low employee cost
- 401k matching
- Wellness benefits
- Competitive salary and success sharing bonus
- Flexible vacation, plus sick time and holidays
- Entrepreneurial culture
- Contributions rewarded
- Camera-enabled virtual interviews
- Equal employment opportunity and disability accommodations