Sr. Data Scientist

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.

Job Title: Sr. Data Scientist

Job Description:

The Sr. Data Scientist will be an integral part of McKesson’s Generics Analytics team and will be responsible for developing and implementing data-driven strategies that enhance the efficiency and effectiveness of pricing decisions made within McKesson's Generics organization. The Sr. Data Scientist should be able to work on complex business problems by leveraging our in-house data sources within a collaborative environment. This role will work in cross-functional data and enablement teams, to uncover insights, optimize processes, provide recommendations, and improve pricing related decision-making. They will collaborate across the team to build and deliver on broad data, analytics and digital organizational capabilities related to pricing transformation. They should be able to identify data and insights-based opportunities for enhancing Generics business performance, testing and validating various hypothesis and then industrializing the AI/ML solution pipeline. They will critically evaluate on what can be done with data, where are the opportunities to monetize data, how can data-based insights be incorporated in existing business processes and systems. This role will drive delivery excellence and will lead strengthening trust across multiple stakeholders. The ideal candidate will be an expert in analytics and AI/ML-based pricing of pharmaceutical products with deep understanding of healthcare distribution and supply chain. 

Key Responsibilities:

• Lead development of analytical and data science solutions for different Generics business stakeholders
• Utilize advanced analytics, machine learning, and predictive modeling to identify and uncover opportunities for price optimization, margin improvement, revenue acceleration, reducing leakage / revenue loss, product replacement, demand and revenue forecasting, reducing stockouts and optimizing inventory.
• Collaborate with business stakeholder & technology teams to identify existing, or new, areas that can benefit significantly from advanced analytics and data science.
• Communicate strategy and results to technical and non-technical audiences / develop and maintain strong relationships with key stakeholders, partners, and internal clients .
• Streamline AI/ML delivery process with “fail fast” approach for experimenting and Agile implementation to scale the solutions that has been proven to be valuable for the business.
• Build AI engineering frameworks, ML systems, data pipelines and security with enterprise value priorities and are deployable.
• Have good understanding and appreciation for data governance and AI governance principles and coach the teams to adopt the guidelines for the same.
• Lead building of comprehensive analytical insights engine that optimizes various pricing outputs across multiple algorithms and scenarios to provide the most optimal recommendation and an ability for the users to perform simulations and make best pricing decisions.
• Drive key AI and analytical initiatives, providing clear timelines and actionable plans for implementation and value generation.
• Lead, motivate, and inspire teams to embrace AIML technologies and contribute to the organization's overall success.
• Leverage CICD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Ensures all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. 
• Continuously monitor and optimize AI models and algorithms to improve performance and reliability.
• Monitor real-time performance of deployed models, analyze performance data, and proactively identify and address performance issues to ensure optimal model performance.
• Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability in a timely and efficient manner.
• Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations.
• Develop