Impress employers and recruiters.
Choose from hundreds of resume examples.
Impress employers and recruiters.
Choose from hundreds of resume examples.
Tailor your resume to this Staff Data Scientist role.
Rezi rewrites your resume against Vi's job description. Free.
Tailor your resume to this Staff Data Scientist role.
Rezi rewrites your resume against Vi's job description. Free.
Don't guess if your resume is good enough.
See how it scores against the Staff Data Scientist posting at Vi — free, in seconds.
Don't guess if your resume is good enough.
See how it scores against the Staff Data Scientist posting at Vi — free, in seconds.
About the Role
Vi Engage integrates predictive models into the operations of major health systems and health plans to enhance care navigation and specialty capture. This role focuses on building the underlying modeling engine that powers these deployments, transforming longitudinal data into patient utilization predictions and preventative care enrollment propensity scores. The core objective is to create a configurable system that trains, selects, scores, and delivers models for any customer, automating feature engineering and model selection to achieve broad generalization across deployments.
Responsibilities
- Own ML pipelines built for scale, featuring config-driven processes for training, scoring, and delivering predictions from large longitudinal datasets.
- Develop mass customization capabilities through automatic feature engineering and model selection, producing customer-specific models without bespoke engineering.
- Achieve cross-customer generalization by identifying modeling improvements applicable to all deployments.
- Build foundational DS components that Forward Deployed Data Scientists use to accelerate customer deployments.
- Productize capabilities by transforming pilots and one-off proofs into robust features that scale to multiple customers.
Requirements
- Proven experience modeling longitudinal data in production, having shipped uplift, survival, or propensity models that influenced organizational spending or outreach.
- Experience transitioning a pilot or proof of concept into a repeatable product that has been successfully deployed to multiple customers.
- Deep understanding of data science principles, including segmentation, campaign optimization, and uplift estimation strategy.
- Proficiency in Python and the associated ML engineering stack (pandas, sklearn, PySpark, airflow).
- Experience with MLOps tooling such as mlflow, SageMaker, or comparable platforms for model tracking and deployment.
- Proficiency in leveraging AWS cloud services (S3, Glue, EMR, MWAA, SageMaker) for scalable model deployment.
Skills
- Python
- pandas
- sklearn
- PySpark
- airflow
- mlflow
- SageMaker
- AWS
- S3
- Glue
- EMR
- MWAA
- Segmentation
- Campaign optimization
- Uplift estimation
Location
- Remote
Work Type
- Full-time
Experience Level
- Applied, in-production role
- Hands-on architecting and building
About the Company
- Vi Engage integrates predictive models into the daily operations of the largest health systems and health plans in the country.
- The company drives care navigation, specialty capture, and associated workflows through data-driven insights.