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Vi

Staff Data Scientist

Vi

. Build config-driven machine-learning pipelines that train, select, score, and deliver predictions from large longitudinal datasets .

Posted 9/15/2026full-timeRemote • Massachusetts • United StatesLeadWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and deploying machine-learning models, particularly in healthcare applications, with a strong focus on data-driven decision-making and model optimization. Proficient in Python and associated libraries, capable of productizing data science solutions for diverse customer needs.

Highest-signal resume keywords
Machine-Learning Pipeline DevelopmentModel Tracking and DeploymentPython ProgrammingAWS Data ServicesHealthcare Compliance Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Machine-LearningFeature EngineeringModel SelectionSegmentationCampaign OptimizationUplift ModelingData ScienceModel EvaluationData GovernanceEfficacy Study Design
Soft Skills
Analytical ThinkingCommunicationProblem-Solving
Tools & Technologies
PythonPandasScikit-LearnPySparkAirflowMLflowSageMakerAWS S3AWS GlueAWS EMR
Industry Keywords
HealthcareLife SciencesHIPAAData Governance Frameworks

Tech Stack

Tools & technologies
AirflowAWSPandasPySparkPythonScikit-Learn

About the role

Key responsibilities & impact
  • Build config-driven machine-learning pipelines that train, select, score, and deliver predictions from large longitudinal datasets
  • Convert claims, EHR, lab, and online behavioral-intent data into predictions about healthcare utilization and preventative-care enrollment propensity
  • Develop automatic feature engineering and model selection for customer-specific models without customer-specific engineering
  • Find modeling improvements that generalize across customer deployments
  • Implement and maintain data-science components used by Forward Deployed Data Scientists in customer deployments
  • Productize pilots and one-off proofs so capabilities remain effective across multiple customers
  • Set the modeling standard for customer deployments and own improvements that generalize across deployments
  • Interface occasionally with design-partner clients while primarily working in an applied, in-production, non-customer-facing role
  • Hands-on architect and builder; not a research or management position

Requirements

What you’ll need
  • Personally shipped uplift, survival, or propensity models whose output changed organizational spending or outreach
  • Experience taking pilots or proofs of concept into repeatable products that survived multiple customers
  • Real data science depth in segmentation, campaign optimization, and identification strategy for uplift estimates
  • Ability to defend modeling choices, evaluate models honestly, and select simpler approaches when appropriate
  • Fluent in Python, pandas, scikit-learn, PySpark, and Airflow
  • Experience with model tracking and deployment tooling such as MLflow or SageMaker
  • Ability to run models at scale in AWS using S3, Glue, EMR, MWAA, and SageMaker
  • Healthcare or life sciences domain knowledge is nice to have
  • Familiarity with HIPAA, healthcare compliance, and data governance frameworks is nice to have
  • Experience designing pilots and efficacy studies tied to business outcomes is nice to have
  • Experience building internal platforms or frameworks is nice to have

Benefits

Comp & perks
  • No explicit benefits, perks, or compensation extras stated