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What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you.
Job Summary
We are seeking a highly skilled and motivated data scientist with a strong background in Real-world Data (RWD) analytics. The ideal candidate will have expertise in programming for the analysis of large datasets and a proven track record of research in healthcare. You will collaborate with colleagues across McKesson’s business, data, and technology teams, to provide analytical support and methodological expertise for Real-World Evidence (RWE) studies, generation of input parameters for health economic models, and exploratory RWE requests from cross-functional stakeholders.
Specific Responsibilities
You will independently and collaboratively plan, perform, and communicate the following outcomes research support activities:
- Develop Statistical Analysis Plans (SAPs) and/or table shells as part of study protocol development
- Explore the suitability of datasets (internal or external) for addressing outcomes research questions
- Assist in RWE analytics activities, including writing detailed documentation, reviewing, running, and validating analyses in SAS, R, and/or SQL
- Propose methodologies, data sources, and process improvements, leveraging your understanding of the RWD ecosystem and your machine learning and statistical expertise.
- This role requires the ability to analyze complex data sets, develop algorithms, and create innovative solutions to enhance our data-driven decision-making processes.
General Responsibilities
- Collaborate: Partner with team members and lead analytics segments of RWD and RWE projects, utilizing PSP, pharmacy, clinic, claims, and other data sets
- Data Analysis: Analyze and interpret large RWD databases
- Develop and Review Specifications: Collaborate and communicate effectively with colleagues to develop and review programming specifications, ensuring a comprehensive understanding of the RWE study
- Develop Reusable Code: Efficiently develop and implement reusable analytics code in SAS, R, SQL for analyzing large databases, and provide clear documentation
- Present Results: Clearly and effectively present results and methodologies to stakeholders and general audiences.
- Drive Innovation: Contribute to driving innovation in RWD/RWE analytics with an entrepreneurial spirit, sharing knowledge and ideas to accelerate and improve RWE deliverables from RDAI.
Minimum Qualifications
- An advanced degree (MA/MS, PhD, ScD, etc.) in a quantitative field such as statistics, biostatistics, econometrics, data science, biomedical informatics, health informatics, clinical informatics, epidemiology, health economics, biometrics, operations research, engineering, computer science, or similar fields with at least 2 years of relevant data science and programming experience.
- A minimum of two years of hands-on professional data science experience in RWE analytics, including writing, reviewing, running, and validating conventional and advanced programs, and generating analytic files through SAS, R, or SQL. Knowledge of Python programming is a plus
- Broad and in-depth experience with RWD such as claims, EMR, pharmacy, patient registry, etc. to generate RWE.
- Experience in identifying research cohorts using various classification codes such as ICD-9-CM, ICD-10-CM, SNOMED, LOINC, NDC, HCPCS, CPT, etc.
Key Competencies
- Ability to understand and write statistical analysis plans, translating observational research objectives into programming specifications.
- Strong attention to detail, clarity, accuracy, and conciseness.
- Ability to plan, organize, and manage multiple analytics tasks simultaneously across different but interrelated therapeutic areas.
- Demonstrated motivation, proactiveness, inquisitiveness, and flexibility in delivering RWE outcomes.
- Foundational understanding of statistical terminology, concepts, and techniques used in descriptive and multivariate analysis.
- Demonstrated ability to collaborate effectively with programmers, statisticians, economists, epidemiologists, outcomes researchers, and IT in a matrix and cross-functional environment.
- Familiarity with machine learning algorithms (e.g., supervised, unsupervised learning, deep neural networks, transfer learning) and ‘Big Data’ technologies, and NLP/LLM methods.
We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here.
Our Base Pay Range for this position
$89,700 - $149,500McKesson is an Equal Opportunity Employer
McKesson provides equal employment opportunities to applicants and employees and is committed to a diverse and inclusive environment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age or genetic information. For additional information on McKesson’s full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page.
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