Job Description
Seeking a Data Scientist to develop analytic solutions across McKesson, applying data science methodologies to interdisciplinary business problems.
The Data Scientist role will support the development of analytic solutions across McKesson. Our team applies data science methodologies to interdisciplinary business problems across Finance, Operations, Accounting, & Supply Chain. This position will work closely with multiple business units such as Treasury, FP&A, Operations, and Pricing. The position's objectives are:
- Develop machine learning solutions to support new business initiatives / facilitate next best actions
- Architect and lead implementation of driverless forecasting systems, leveraging best data science practices and technologies
- Lead DevOps of new and existing models, leveraging cloud / open-source technologies
The candidate should possess the ability to perform statistical modelling techniques and derive business insights that are required to drive analytic innovation at McKesson. The candidate should also be an active learner able to grasp and apply new analytic approaches, as well as mentor junior / developing resources.
The purpose of this position is to architect, implement, drive adoption, and measure impact of innovative analytic solutions at McKesson, as well as make significant improvements to existing solutions.
- Develop inventory optimization / multi-echelon simulation framework for supply chain
- Support Transportation organization's forecasting and optimization needs around route selection and late delivery prediction
- Fully automate existing forecasting solutions
- Design and guide implementation of model variance analysis and impact tracking framework
- Lead in deploying statistical models in production
- Lead in development of statistical simulation decision frameworks
Other responsibilities include:
- Support stakeholders' analytic needs, gather user requirements, help drive adoption
- Cultivate business development opportunities
- Assist in developing and maintaining long-term stakeholder relationships and networks
Requirements:
- 5+ years data science / analytics / programming experience based on combination of industry and academic experience
- Bachelor's degree in a technical field such as: Computer Science, Statistics, Applied Mathematics, Finance, Economics or related quantitative / STEM majors. Masters and/or PhD preferred.
Critical skills:
- Demonstrated ability to tackle problems across the full data stack, from data wrangling (leveraging SQL or other methodologies) to stakeholder consumption at scale
- Deep knowledge of machine learning / data science best practices
- Knowledge of statistical programming (Python, R)
- Ability to communicate technical concepts to non-technical audiences
- Demonstrated experience with object-oriented programming (Python, Java, C#, VBA, etc.)
- Strong grasp of fundamental statistical concepts: linear regression, A/B testing, outlier analysis, probability distributions, tests for independence, etc.
Additional knowledge & skills:
- Analysis/Process Thinking
- Team player
- Strong verbal and written communication
- Knowledge of relational databases (e.g. MS SQL Server, Snowflake, Oracle)
- Knowledge of cloud computing platforms is a plus (e.g. Azure, AWS, Google Cloud, Databricks)
- Proficient with Excel spreadsheets, financial modeling, and reporting
- Prior data mining experience using enterprise systems (SAP or JD Edwards preferred)
- Knowledge of data warehousing & ETL best practices is a plus