Senior Data Scientist, Analytics & Informatics

UPMC•Pittsburgh, PA

16h•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 Senior Data Scientist on the DAI team will lead the design, development, and deployment of complex analytical models supporting health outcomes research, product development, and strategic business initiatives. The Senior Data Scientist will collaborate closely with clinical faculty, subject matter experts, and business leaders to address pressing healthcare challenges, serving as a key technical expert and mentor for junior team members. This role draws heavily on advanced data science techniques, predictive modeling, and medical informatics, with an emphasis on delivering actionable insights and driving innovation. Please note that this position is in-office 3 days per week.

Requirements

Master's in health economics, data science, statistics, computer science or related field, with at least 5 years of experience in developing, implementing and overseeing models related to health services/ outcomes research and medical information programs or related work experience; OR, PhD/MD with training or equivalent terminal degree in health economics, data science, statistics, computer science or related field, with at least 3 years of experience in developing, implementing and overseeing models related to health services/outcomes research and medical information programs or related work experience.
Comparable combination of education and experience will be considered in lieu of the above stated qualifications.
Demonstrated expertise in relevant applied analytical methods in healthcare (payor/provider).
Demonstrate prior independent application of data science methods specifically to healthcare industry data.
Ability to leverage cutting-edge data science experience from other industries (e.g., population segmentation, risk analysis, optimization analysis, real-time analytics) to advance healthcare analytics will be strongly considered in lieu of health care experience.
Advanced proficiency in clinical and scientific research methodologies to generate research questions, query complex clinical data to conduct descriptive and predictive analysis that create new insights to address UPMCE's business needs.
Experience with cloud-based data platforms and tools (e.g., AWS, Azure, GCP) to build, deploy, and scale models.
Strong understanding of observational study designs, confounding control, and real-world data (RWD) analytics.
Familiarity with data visualization tools (e.g., Tableau, Power BI) for creating impactful reports.
Effective data analysis and interpretation skills with ability to draw and present quantitative conclusions leveraging graphs, and other visualizations to enable rapid understanding of clinical data to deliver business insights.
Excellent verbal and written communication skills to translate complex concepts into actionable insights for diverse stakeholders, including senior leadership and customers.
Ability to represent analytics methodologies, findings, and recommendations in cross-functional forums, influencing the team leadership's decision-making.
Exceptional interpersonal skills, and entrepreneurial orientation characterized by pragmatism, independence, self-determination, and an agile, flexible behavior style.
Excellent communicator with ability to prepare and deliver clear scientific and business communication materials (documents, presentations) for internal and external facing activities.
Ability to influence team leadership and customers through effective communication of data science methods and study results.
Demonstrated understanding of the differences between business requirements, scientific rigor, and technical constraints with ability to distill complex issues and ideas down to simple comprehensible terms.
Demonstrated understanding of financial metrics and cost efficiencies that have a positive business impact.
Proven track record of successfully leading and delivering large projects in fast-paced, matrixed environments.
Strong mentoring skills, with the ability to guide junior team members in adopting best practices and improving their technical capabilities.
Exceptional time management, organizational, and prioritization skills with experience in agile/scrum methodologies.
Self-driven, scientifically curious individual who thrives in a high pace, and rapidly evolving business environment that supports entrepreneurs and founders.

Nice To Haves

Background in applying AI/ML to real-time analytics, optimization, or population health segmentation.
Experience with advanced analytics research infrastructure and platforms.
Previous publications or presentations at scientific conferences showcasing successful data science initiatives.

Responsibilities

Conceptualize and implement complex statistical and machine learning models to evaluate health outcomes, cost-effectiveness, and other critical business needs.
Collaborate with clinical faculty and subject matter experts to identify research hypotheses and conduct rigorous analyses using large real-world healthcare data sets.
Provide guidance and mentorship to junior and mid-level Data Scientists, reviewing their work, offering technical support, and promoting best practices.
Facilitate knowledge-sharing sessions to encourage professional growth, foster collaboration, and maintain high-quality standards within the team.
Independently manage large-scale data science projects, including scoping, planning, execution, monitoring, and stakeholder communication.
Collaborate cross-functionally to align data-driven initiatives with strategic objectives, ensuring timely delivery of insights and solutions that address business challenges.
Serve as a trusted advisor by proactively identifying opportunities to apply advanced analytics for improved clinical and financial outcomes.
Review data extraction processes using electronic medical records (EMRs) and additional healthcare data sources to generate high-fidelity datasets for analysis.
Design and maintain end-to-end analytical pipelines, including pre-processing, validation, feature engineering, model development, and performance monitoring.
Apply observational study designs, advanced causal inference methods (e.g., propensity scores, handling missing data), and statistical techniques relevant to healthcare research.
Explore, evaluate, and integrate emerging technologies (e.g., AI/ML frameworks, cloud-based tools) to continuously improve modeling efficiency and scalability.
Present findings through clear, compelling visualizations and narratives that resonate with both technical and non-technical audiences, including senior executives.
Contribute to manuscripts, abstracts, posters, and conference presentations, demonstrating the impact of advanced analytics in healthcare.
Stay abreast of industry trends, research advances, and best practices in data science and healthcare analytics.
Proactively share insights and implement innovative analytics methods to maintain UPMCE's position as a thought and technical leader in the healthcare data science space.