Data Scientist

at  McKesson

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care.
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.

Analytic Responsibilities

• Develop inventory optimization / multi-echelon simulation framework for supply chain.
• Lead in development of statistical simulation decision frameworks.
• Build time series models leveraging techniques such as Sarima, Prophet, Holt-Winters, Transformers.
• Extract, analyze, and interpret large volumes of structured and unstructured data from various sources.
• Perform advanced analytics functions (e.g., XGBoost, SVM, Random Forest, Cluster Analysis, etc.) to create insights that in turn provide meaningful results.
• Design and execute experiments to test hypotheses and validate models.
• Collaborate with Engineering team to automate machine learning models by developing comprehensive documentation (e.g. coding, workbooks, etc.).
• Build prototypes for fast delivery to understand different business solutions.
• Work within an agile framework
• Develop an A/B testing framework by conducting hypothesis-driven tests to enhance products and services.
• Develop a monitoring mechanism for all ML projects (e.g. data drift, model drift).
• Familiarity with data engineering concepts such as data ingestion, ETL (Extract, Transform, Load), and data modeling to ensure efficient data pipelines in Databricks.
• Proficiency in Databricks, including experience with Databricks notebooks, clusters, and job scheduling as a plus

Other Responsibilities

• Present findings and insights to non-technical stakeholders in a clear and concise manner.
• Cultivate business development opportunities.
• Assist in developing and maintaining long-term stakeholder relationships and networks.

At McKesson, we care about the well-being of the patients and communities we serve, and that starts with caring for our people. That’s why we have a Total Rewards package that includes comprehensive benefits to support physical, mental, and financial well-being. Our Total Rewards offerings serve the different needs of our diverse employee population and ensure they are the healthiest versions of themselves.
As part of Total Rewards, we are proud to offer a competitive compensation package at McKesson. 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.

Analytic Responsibilities

• Develop inventory optimization / multi-echelon simulation framework for supply chain.
• Lead in development of statistical simulation decision frameworks.
• Build time series models leveraging techniques such as Sarima, Prophet, Holt-Winters, Transformers.
• Extract, analyze, and interpret large volumes of structured and unstructured data from various sources.
• Perform advanced analytics functions (e.g., XGBoost, SVM, Random Forest, Cluster Analysis, etc.) to create insights that in turn provide meaningful results.
• Design and execute experiments to test hypotheses and validate models.
• Collaborate with Engineering team to automate machine learning models by developing comprehensive documentation (e.g. coding, workbooks, etc.).
• Build prototypes for fast delivery to understand different business solutions.
• Work within an agile framework
• Develop an A/B testing framework by conducting hypothesis-driven tests to enhance products and services.
• Develop a monitoring mechanism for all ML projects (e.g. data drift, model drift).
• Familiarity with data engineering concepts such as data ingestion, ETL (Extract, Transform, Load), and data modeling to ensure efficient data pipelines in Databricks.
• Proficiency in Databricks, including experience with Databricks notebooks, clusters, and job scheduling as a plu

Other Responsibilities

• Present findings and insights to non-technical stakeholders in a clear and concise manner.
• Cultivate business development opportunities.
• Assist in developing and maintaining long-term stakeholder relationships and networks