Data Scientist

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 Data Scientist will provide insights on the design and execution of health outcomes research studies, including retrospective database analysis, and meta-analysis. The Data Scientist will work closely with the Director of Analytics & Informatics to develop and implement advanced analytics techniques, medical informatics and predictive modelling methodologies to support UPMCE's business needs.

Hybrid- Minimum 2 days on-site. Standard 9am-5pm eastern time work hours. (flexibility with manager approval)

Responsibilities:
•   Understand key business drivers and support development of an analytical framework that delivers actionable insights for UPMCE and our portfolio companies.
•   Work collaboratively with product teams to ensure a shared understanding of business objectives and define what new insights are needed to support these objectives.
•   Become a trusted analytics partner for UPMCE by identifying areas that will enhance the organization's analytical toolkits to deliver data-driven insights for leadership, influence decision making, drive revenue and growth, while bridging knowledge gaps.
•   Participate in discussions with clinical faculty, subject matter experts, business leaders to discuss current issues in medical practice and develop research hypotheses.
•   Hands-on data extraction and database analysis using the electronic medical record and other relevant data sources.
•   Serve as an expert consultant regarding advanced data mining and predictive modeling methods, as well as application of scientific principles of knowledge discovery.
•   Contribute to development and review of study protocols, Analysis plans, and other documents related to the execution of clinical, health economics and outcomes research studies and models.
•   Develop strong problem-solving skills in the creation and interpretation of quantitative analyses and predictive models and assist the Director in the development of Analysis plans.
•   Demonstrate ability to independently design rigorous clinical, financial, and quality analyses grounded in data science.
•   Contribute to the write-ups, including relevant portions of manuscripts, abstracts, posters, slide presentations.
•   Author and present studies at scientific conferences and other appropriate venues on the behalf of the study team, as needed.
•   Analytical Modeling & Research
•   Design and implement statistical or machine learning models (e.g., regression, classification) that address clinical and business priorities.
•   Design and implement statistical or machine learning models (e.g., regression, classification) that address clinical and business priorities.
•   Participate in framing research hypotheses, creating analysis plans, and applying appropriate methodologies for observational or retrospective studies.
•   Work closely with clinicians and subject matter experts to identify real-world healthcare challenges, leveraging data science to propose evidence-based solutions.
•   Team Collaboration & Mentorship: Offer guidance to junior team members and promote best practices in data cleaning, model development, and interpretation of findings.
•   Partner with product managers, engineers, and other stakeholders to clarify goals, refine requirements, and ensure cohesive teamwork.
•   Contribute to code and study plan reviews, helping elevate the overall quality and reliability of analytics outputs.
•   Project & Stakeholder Management: Scope and prioritize analytics tasks in line with broader project timelines, keeping stakeholders informed of milestones and deliverables.
•   Collaborate with business leads, clinical faculty, and external partners to confirm that analyses align with strategic objectives and user needs.
•   Identify potential roadblocks: such as data quality or resource constraints and proactively propose solutions to maintain project momentum.
•   Data Extraction & Analysis: Employ SQL or equivalent tools to extract data from electronic medical records and other large databases, ensuring accuracy and consistency.
•   Clean, transform, and merge datasets, addressing missing values, outliers, or anomalies that could distort analyses.
•   Conduct initial statistical tests, descriptive analysis, and visual exploration to uncover trends or patterns critical to business decisions.
•   Advanced Analytics & Methodology: Choose appropriate analytical techniques (propensity scoring, imputation, clustering, etc.) to address varying project needs.
•   Stay proficient in relevant libraries and frameworks for efficient and scalable modeling.
•   Explore emerging technologies (e.g., cloud-based AI/ML platforms) to enhance performance, reduce costs, or gain novel insights.
•   Communication & Knowledge Translation: Translate complex results into accessible narratives and visualizations for both technical and non-technical audiences.
•   Synthesize key findings into concise presentations or reports for the Director, highlighting business implications and recommendations.
•   Contribute to internal documentation, external-facing reports, and (if applicable) manuscripts or conference materials to showcase insights.
•   Continuous Improvement & Thought Leadership: Remain current on industry best practices, new analytic methods, and healthcare trends that may impact data-driven strategies.
•   Suggest and implement improvements to existing workflows (e.g., data pipelines, model deployment processes) for greater reliability and scalability.
•   Evaluate new research or product ideas, proactively identifying opportunities to push boundaries and elevate organizational analytics maturity.
•   Education & Background: Master's 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; OR, PhD/MD with training or equivalent terminal degree in health economics, data science, statistics, computer science or related field, with 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.
•   Demonstrated expertise in relevant applied analytical methods in healthcare (payor/provider).
•   Demonstrate prior independent application of data science methods specifically to healthcare industry data at the expert level.
•   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 Analytics Skillset
•   Proficiency in clinical and scientific research methodologies, particularly in the design of observational or real-world data studies.
•   Hands-on experience with data extraction (e.g., SQL) and statistical software (R, Python, SAS) for descriptive and predictive analysis.
•   Familiarity with confounding control, missing data handling, and other statistical techniques relevant to large healthcare data sets or comparable real-world data.
•   Working knowledge of cloud-based platforms (AWS, Azure, or GCP) for developing, deploying, and scaling analytical models is an advantage.
•   Communication & Stakeholder Interaction: Effective data storytelling skills, including the ability to present quantitative findings using clear visualizations and well-structured narratives for both technical and non-technical audiences.
•   Strong collaboration abilities to engage with clinical faculty, business leaders, and cross-functional teams
•   translating technical methods into actionable insights.
•   Demonstrated consultative approach in advising stakeholders on the selection of analytic methods, feasibility of proposed research, and interpretation of results.
•   Business: Understanding of healthcare-specific business drivers (e.g., cost optimization, patient outcomes, revenue growth) and how data science can address them.
•   Capacity to balance scientific rigor and real-world constraints, tailoring advanced methods to meet practical organizational objectives.
•   Appreciation for financial metrics and cost efficiencies, with the ability to identify data-driven opportunities for revenue enhancement or process improvement.,
•   Project Management: Experience executing medium- to large-scale projects, including coordinating timelines, deliverables, and stakeholder communication in fast-paced or matrixed environments.
•   Adaptability to evolving priorities and the agility to shift focus or methodology as needed to meet project goals and stakeholder expectations click apply for full job details