AI Data Engineer/ Data Scientist

Overview

We are seeking an AI Data Engineer / Data Scientist to help build and support data products focused on patient journey optimization and pre-call intelligence. This role combines data engineering, feature engineering, and applied machine learning to transform enterprise data into actionable insights and production-ready analytical solutions.

The ideal candidate has hands-on experience with Snowflake, SQL, Python, and Dataiku, along with Snowpark or PySpark experience and a working knowledge of machine learning. You will collaborate with data scientists and cross-functional teams to build reliable data pipelines, develop feature datasets, and support the delivery of scalable, data-driven products in a governed enterprise environment.

Key Responsibilities

• Data Engineering & Transformation: Query, transform, and optimize data from Snowflake, working with existing star-schema tables to support analytical and machine learning use cases.
• Feature Engineering: Build and maintain feature datasets, tables, and views in Snowflake to support downstream analytical workflows and model development.
• Dataiku Development: Create and maintain analytical workflows and features in Dataiku for use by data science teams and machine learning models.
• Machine Learning: Design, build, validate, and support machine learning models for patient journey optimization and pre-call intelligence use cases.
• Feature Store Management: Evaluate existing features in the Feature Store before creating new ones, promoting reuse and consistency across analytical products.
• Data Product Support: Develop and maintain analytical data products and reporting datasets that support business intelligence and decision-making.
• Data Quality & Reliability: Implement testing, validation, monitoring, and performance optimization practices to ensure data accuracy and pipeline reliability.
• Pipeline Support: Troubleshoot data issues and assist with upstream data pipelines and integrations as needed.
• Cross-Functional Collaboration: Partner with data scientists, data engineers, analytics teams, and business stakeholders to translate requirements into effective technical solutions.

Required Qualifications

• Hands-on experience with Snowflake for data storage, transformation, querying, and analytical data development.
• Strong SQL skills, including the ability to write and optimize complex queries for data transformation and feature engineering.
• Proficiency in Python for data processing, analytical workflows, and machine learning applications.
• Experience with Snowpark or PySpark for data processing and transformation.
• Practical experience with Dataiku for analytical workflows, feature development, model scoring, or machine learning processes.
• Familiarity with machine learning concepts and experience building, validating, or supporting ML models.
• Experience developing and maintaining production data pipelines and reusable datasets.
• Knowledge of data modeling, data quality testing, monitoring, and CI/CD practices.
• Ability to work effectively in a governed enterprise environment with established data standards and controls.
• Strong analytical, problem-solving, communication, and collaboration skills.

Preferred Qualifications

• Experience in healthcare, healthcare claims, patient support, provider data, or healthcare analytics.
• Familiarity with healthcare professional (HCP) data or Veeva platforms.
• Experience with AWS data services, including Lambda, S3, Glue, and Step Functions.
• Familiarity with feature stores, feature reuse, and production machine learning workflows.
• Experience with Power BI or other enterprise business intelligence and reporting tools.
• Experience supporting data products that drive predictive analytics, customer insights, or workflow optimization.

What We're Looking For

We are looking for a technically strong, hands-on professional who can bridge data engineering and data science. The ideal candidate is comfortable working across data transformation, feature development, and machine learning workflows, with a focus on building reliable, reusable, and scalable data products that deliver meaningful business value.