Job Description 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 actionsArchitect and lead implementation of driverless forecasting systems, leveraging best data science practices and technologiesLead DevOps of new and existing models, leveraging cloud / open-source technologiesThe 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.
Position Description 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.
Analytic Responsibilities Develop inventory optimization / multi-echelon simulation framework for supply chainSupport Transportation organization's forecasting and optimization needs around route selection and late delivery predictionFully automate existing forecasting solutionsDesign and guide implementation of model variance analysis and impact tracking frameworkLead in deploying statistical models in productionLead in development of statistical simulation decision frameworksOther Responsibilities Support stakeholders' analytic needs, gather user requirements, help drive adoptionCultivate business development opportunitiesAssist in developing and maintaining long-term stakeholder relationships and networksMinimum Requirements Experience: 5+ years data science / analytics / programming experience based on combination of industry and academic experience
Education: 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 scaleDeep knowledge of machine learning / data science best practicesKnowledge of statistical programming (Python, R)Ability to communicate technical concepts to non-technical audiencesDemonstrated 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 ThinkingTeam playerStrong verbal and written communicationKnowledge 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 reportingPrior data mining experience using enterprise systems (SAP or JD Edwards preferred)Knowledge of data warehousing & ETL best practices is a plus
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