Technical Project Manager - Data Science

Data Science-Machine Learning Engineer

As a Project Manager, you will lead a strategic healthcare initiative with a strong focus on STARS performance and outcomes. You will drive a cross-functional program involving Machine Learning, Data Engineering, and data intelligence, ensuring seamless collaboration between business stakeholders and technical teams.

In this role, you will be responsible for translating business objectives into actionable delivery plans, aligning data, analytics, and reporting workstreams to support quality improvement and operational efficiency. You will work closely with stakeholders, product teams, and engineering teams to identify opportunities, prioritize initiatives, and deliver scalable data-driven solutions that enhance performance and decision-making aligned with STARS goals.

Essential Skills and Qualifications:

1. 5+ Years of experience in AI/ML, Data Science, or Data Engineering roles with team leadership
2. Strong understanding of Machine Learning and Data Science concepts, including model development, feature engineering, and evaluation
3. Proficiency in Python and PySpark for large-scale data processing and model development
4. Experience in building and managing end-to-end data pipelines (ETL/ELT)
5. Experience with MLOps practices, including model deployment, monitoring, and lifecycle management
6. Strong understanding of data quality, governance, and scalable data architecture
7. Experience leading cross-functional teams including ML engineers, data engineers, and BI developers
8. Exposure to visualization tools like Power BI and ability to connect insights to business KPIs
9. Strong understanding of healthcare data and STARS metrics (preferred)
10. Excellent communication and stakeholder management skills
11. Strong analytical, problem-solving, and decision-making abilities
12. Ability to translate complex business problems into data-driven AI/ML solutions

Qualifications:

1. 5+ Years of experience in AI/ML, Data Science, or Data Engineering roles with team leadership
2. Strong understanding of Machine Learning and Data Science concepts, including model development, feature engineering, and evaluation
3. Proficiency in Python and PySpark for large-scale data processing and model development
4. Experience in building and managing end-to-end data pipelines (ETL/ELT)
5. Experience with MLOps practices, including model deployment, monitoring, and lifecycle management
6. Strong understanding of data quality, governance, and scalable data architecture
7. Experience leading cross-functional teams including ML engineers, data engineers, and BI developers
8. Exposure to visualization tools like Power BI and ability to connect insights to business KPIs
9. Strong understanding of healthcare data and STARS metrics (preferred)
10. Excellent communication and stakeholder management skills
11. Strong analytical, problem-solving, and decision-making abilities
12. Ability to translate complex business problems into data-driven AI/ML solutions