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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 what can be done with data, where the opportunities to monetize data are, and how data-based insights can 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 a 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 stakeholders & 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 and develop and maintain strong relationships with key stakeholders, partners, and internal clients.
- Streamline AI/ML delivery process with a “fail fast” approach for experimenting and Agile implementation to scale the solutions that have been proven to be valuable for the business.
- Build AI engineering frameworks, ML systems, data pipelines, and security with enterprise value priorities that 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 a 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.
- Ensure 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 and maintain documentation, standard operating procedures, and guidelines related to MLOps processes, tools, and best practices.
- Support stakeholders’ analytic needs, gather user requirements, and help drive adoption.
- Manage business stakeholder relationships to drive action and value from data science insights.
- Assist in developing and maintaining long-term stakeholder relationships and networks.
Critical / Must have skills:
- 8+ years of data science and advanced analytics experience.
- 4+ years of healthcare / pharmaceutical / distribution experience.
- 2+ years of pricing analytics and data science modeling experience.
- Proven record of building data and analytics models and solutions in the pricing space, preferably in the healthcare, pharmaceutical, and/or supply chain domain.
- Previous experience in managing complex analytical and data science projects to deliver business outcomes.
- Demonstrated ability to tackle problems across the full data stack, from data wrangling (leveraging SQL or other methodologies) to stakeholder consumption at scale.
- Strong grasp of fundamental statistical concepts: linear regression, A/B testing, outlier analysis, probability distributions, tests for independence, etc.
- Knowledge of machine learning / data science best practices.
- Demonstrated experience in data visualization skills (PowerBI, Tableau, RShiny).
- Experience in developing cloud computing data and analytic platforms (Snowflake, Databricks, Azure, AWS, GCP etc).
- Deep knowledge of advanced analytics and big data technologies and methodologies, as well as proven experience translating big data into actionable outcomes in healthcare / pharmaceutical / biotech industries.
- Proven track record of solving complex problems, thinking creatively, and using data to tell a story to influence senior level stakeholders.
- Outstanding verbal and written communication with all levels of management, ability to translate technical terms into business insights.
- Ability to work in a matrix environment and build partnerships with both internal and external stakeholders.
- Very strong team player.
- Strong verbal and written communication.
- Bachelor’s degree in Data Science, Statistics, Computer Science, applied mathematics, machine learning, or a related data-centric technical field. Master’s degree preferred.
Preferred skills:
- Industrializing AI/ML based solutions by creating a homegrown analytics-based decisioning engine/platform.
- Understanding of financial statement analysis and cost accounting principles and/or experience in financial accounting / FP&A related analytics in the healthcare distribution space.