Purpose:
The Technology Solutions team offers technical and business services for UPMCE portfolio companies and investment partners creating innovative healthcare solutions to drive clinical and financial outcomes. We support all stages of a healthcare technology venture’s lifecycle with strategic, implementation, and operational services. The Data Analytics and Informatics Service within the Technology Solutions team provides key data-driven insights for both Digital Solutions and Translational Sciences focus areas to address critical business questions supporting investment and product development life cycles. The Associate Data Scientist will contribute to data-driven solutions that guide product development, operations, and strategic decision-making across UPMCE. Working under the guidance of experienced team members, the Associate Data Scientist will apply fundamental data science methods and collaborate with diverse technical and clinical professionals to deliver impactful outcomes. The Associate Data Scientist will be responsible for supporting the development of data models, exploring data to uncover insights, and applying machine learning techniques under the mentorship of senior data scientists. The Associate Data Scientist will also participate in end-to-end data pipelines, from pre-processing to feature engineering, basic model training, and deployment. This role emphasizes hands-on learning and collaboration, allowing the Associate Data Scientist to build foundational data science skills while delivering actionable insights that drive innovation in healthcare.

Please note that this position is in-office 3 days per week. 

Responsibilities:
 

• Data Preparation & Analysis 
  • Data preparation & maintenance: Assist in troubleshooting, cleaning, and updating data models to ensure accuracy and reliability.
  • Pipeline participation: Contribute to the development of end-to-end data analysis pipelines, including data pre-processing, feature engineering, and model deployment.
  • Ad-hoc analyses: Work with team members to produce exploratory analyses that uncover trends or patterns useful for business and clinical insights.
• Model Development
  • Basic modeling & research: Under guidance, apply fundamental machine learning concepts and tools (e.g., regression, classification, clustering) to solve real-world health care challenges.
  • Support the development and training of machine learning models and NLP solutions under supervision – for example, helping to build text classification or named-entity recognition models to extract insights from clinical narratives. 
  • Experiment with algorithms and techniques (e.g., regression, classification, basic neural networks) using Python or R, and fine-tune model parameters with guidance from senior team members to improve performance.
• Communication & Collaboration 
  • Collaborate with teams: Partner with cross-functional teams, including product managers, clinicians, data scientists, engineers, and domain experts to clarify project goals and contribute to solutions, eagerly learning from their expertise and contributing your own insights.
  • Documentation & reporting: Document procedures and findings clearly, using relevant dashboards or data visualization tools to present results to both technical and non-technical audiences.
  • Seek guidance: Collaborate regularly with senior data scientists or team leads to refine your approach, troubleshoot issues, and learn best practices.
• Project Execution & Stakeholder Engagement
  • Contribute to end-to-end analytics projects by executing assigned tasks in the data science pipeline, from data ingestion and feature engineering to model evaluation and validation.
  • Work closely with project leads to ensure timely delivery of project milestones, adapting to feedback and new requirements in an agile environment.
  • Adhere to all UPMC policies and procedures; follow established team communication standards, ensuring clarity, consistency, and professionalism in all communications.
• Continuous Learning & Improvement
  • Continuous learning: Stay curious and informed about industry and organizational trends in data science, seeking mentorship from senior team members to advance your technical and analytical capabilities.
  • Active participation: Engage in daily stand-ups, sprint planning, and retrospectives to align with team objectives and share insights. Embrace mentorship and feedback, continuously developing your technical skillset in analytics, NLP, and AI through hands-on project work and training opportunities.
  • Support & collaboration: Work in collaboration with peers to meet project deadlines, proactively offering help and ideas to strengthen collective outcomes.