Lead Data Science Engineer
About The Position
This position is responsible for the engineering work necessary for successful creation, deployment and managing of AI capabilities. This includes ensuring data quality, creation of new data pipelines, optimization and management of existing data pipelines, ingestion and curation of data sources for analytical purposes, transformation of structured and unstructured data into formats suitable for machine learning and advanced analytics, selection and configuration of AI-specific tools and platforms, integration of AI solutions with internal systems, and management and monitoring of AI models through MLOps tools and model ops practices. As data science and AI products are operationalized, they will work closely on deployment and ensure products are production-ready and run smoothly. They will work closely with IT on implementation of data products where traditional application development support is needed.
Requirements
- Bachelor degree and 5 years of work experience in a computer science, engineering, or related field OR Master’s degree and 4 years of work experience in a computer science, engineering, or related field OR Ph.D. and 2 years of work experience in a computer science, engineering, or related field
- Learning and growth mindset
- Customer-focused
- Interpersonal, verbal and written communication skills
- Proficiency in at least five and mastery in one of the following six areas: data analysis and relational-style query languages; data pipelining and ETL; working with semi structured and unstructured data; a high- level programming language; distributed computing; understanding of healthcare
- Proficiency in iterative development practices
- Independently delivering or leading the delivery of data engineering solutions for multiple complex analytics or data science projects and products
- A track record of independently delivering or leading the delivery of ML engineering capabilities
- Experience in Python-based Data Science frameworks (LangChain, LangGraph, LangFuse)
- Experience in Model evaluation and deployment
- Experience in data curation, prep, training, and fine-tuning of Models
- Experience in evaluation frameworks
- Experience in prompt engineering
- Experience in working with multiple Models
Nice To Haves
- Master degree in a computational field, or Bachelor degree with significant healthcare experience
- Understanding PySpark / Databricks to efficiently work with large data sets
- Azure Cloud Infrastructure / Deployment with emphasis on AI related tooling, Azure ML, Azure OpenAI, etc.
- Experience in Observability Frameworks and Framework Operationalization
- Experience in creation of knowledge graph database (neo4J)
- Experience in working with Small Language Models or custom Models
Responsibilities
- Ensuring data quality
- Creation of new data pipelines
- Optimization and management of existing data pipelines
- Ingestion and curation of data sources for analytical purposes
- Transformation of structured and unstructured data into formats suitable for machine learning and advanced analytics
- Selection and configuration of AI-specific tools and platforms
- Integration of AI solutions with internal systems
- Management and monitoring of AI models through MLOps tools and model ops practices
- Deployment of AI products and ensuring they are production-ready and run smoothly
- Working closely with IT on implementation of data products where traditional application development support is needed
Benefits
- Health and wellness benefits
- 401(k) savings plan
- Pension plan
- Paid time off
- Paid parental leave
- Disability insurance
- Supplemental life insurance
- Employee assistance program
- Paid holidays
- Tuition reimbursement
- Annual incentive bonus plan
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What This Job Offers
Job Type
Full-time
Career Level
Senior
