Associate Data Scientist, Analytics & Informatics

UPMC•Pittsburgh, PA

1d•Hybrid

About The Position

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.

Requirements

Master's degree in data science, mathematics, statistics, computer science or related field, with at least 1 year of experience in developing, implementing and overseeing models related to health services/outcomes research and medical information programs or related work experience.
A comparable combination of education and experience will be considered in lieu of the above- stated qualifications.
Proficiency in at least one programming language used in data science (preferably Python or R) for basic data manipulation, exploratory data analysis, and foundational modeling tasks.
Understanding of fundamental algorithms (e.g., linear/logistic regression, decision trees, clustering).
Ability to use SQL for basic querying; comfortable learning visualization tools (e.g., Tableau, Power BI) to present findings clearly.
Ability to write clean, well-documented code following coding best practices, working within Agile frameworks and collaborative coding workflows.
Strong organizational skills and attention to detail when managing data and executing analyses.
Ability to handle multiple tasks and priorities in a fast-paced, agile project setting, responding well to direction and iterative feedback.
Reliable and self-motivated, with a willingness to take initiative in solving problems and an openness to ask for help when encountering obstacles.
Strong problem-solving mindset with the ability to interpret business questions and propose data-driven solutions.
Capable of contributing to a team-oriented environment communicating clearly with peers and stakeholders, willing to listen actively and share ideas.
Good verbal and written communication skills, with the ability to clearly document procedures and results.
Willingness to learn, take on new challenges, and remain flexible within a fast-paced, evolving environment.

Nice To Haves

Familiarity with health care data (clinical and/or payer) is helpful but not mandatory.
Internship, capstone project, or prior work experience in data science, analytics, or software development, especially in a healthcare or biomedical context.
Participation in data science competitions (Kaggle, etc.), hackathons, or relevant research projects that demonstrate enthusiasm for applied machine learning and NLP.
Familiarity with version control tools (e.g., Git).
Basic knowledge of cloud-based platforms (AWS, Azure, or GCP).
Exposure to Agile development methodologies.

Responsibilities

Assist in troubleshooting, cleaning, and updating data models to ensure accuracy and reliability.
Contribute to the development of end-to-end data analysis pipelines, including data pre-processing, feature engineering, and model deployment.
Work with team members to produce exploratory analyses that uncover trends or patterns useful for business and clinical insights.
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.
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.
Document procedures and findings clearly, using relevant dashboards or data visualization tools to present results to both technical and non-technical audiences.
Collaborate regularly with senior data scientists or team leads to refine your approach, troubleshoot issues, and learn best practices.
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.
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.
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.
Work in collaboration with peers to meet project deadlines, proactively offering help and ideas to strengthen collective outcomes.