Job Description

Sr. Data Scientist - Machine Learning Specialist Headquartered in Dublin, Ohio, Cardinal Health, Inc. (NYSE: CAH) is a global, integrated healthcare services and products company connecting patients, providers, payers, pharmacists and manufacturers for integrated care coordination and better patient management. Backed by nearly 100 years of experience, with more than 50,000 employees in nearly 60 countries, Cardinal Health ranks among the top 20 on the Fortune 500. Department Overview augmentedIntelligence ( augIntel ) builds automation, analytics and artificial intelligencesolutions that drive success for Cardinal Health by creating material savings, efficiencies and revenue growth opportunities.The team drives business innovation by leveraging emerging technologies and turning them into differentiating business capabilities. Job Overview Create and deliver business value at Cardinal Health by influencing and leading the charge of the machine leaning domain across business and IT. Design and develop machine learning and deep learning solutions to maximize business value and effectiveness. Responsibilities Research, evaluate, and implement artificial intelligence algorithms.Ensure projects are delivered in-line with reference architecture, road map and to the defined standards and best practices. Troubleshoot technical issues within ML platforms. Diagnose business needs, analyze business processes, data flows, IT / technical artifacts and extract understanding of how the system or business process works as input for model development. Identify high-value ML business opportunities and work with Business and IT stakeholders to realize business benefit. Use deep subject matter/ functional expertise, influence and process skills to help internal/external customers and stakeholders identify and meet their high priority needs while considering cultural and diversity implications. Establish development and delivery best practices in order for ML implementations to minimize rework and maintenance cost. Build ML architectures that work at Cardinal Health scale. Work across diverse teams, perspectives and opinions and quickly build consensus. Practice and ensure code quality and standards, deployment and change management processes, and ML Ops practices (like retraining, feature engineering, monitoring). Encourage informed risk-taking and act as a catalyst for innovation at Cardinal Health Proactively develop, maintain and evangelize technical knowledge in Machine Learning and adjacent technologies. Guide and support the junior data scientists / machine learning engineers on the team. Core Capabilities: Possess deep functional and technical understanding of the Machine Learning technologies (Google\'s Cloud Platform, custom and COTS-embedded) and leverage it across Cardinal Health\'s large, complex and diverse landscape. Business acumen (strong understanding of how business operates, and how to harness data and analytics to meet business needs) Ability to develop and deploy advanced analytical models and algorithms that drive profitable growth, predictable patterns and areas of opportunity. Maintain a solid understanding of up and downstream impacts implementing solutions using ML technologies. Engage early in project efforts in order to analyze current solutions, provide solution options and recommendations, understand business process impact, provide accurate estimates. Aide adoption of ML solutions within the business by conducting training, coordinating demos. Establish development and delivery best practices in order for ML implementations to minimize rework and maintenance cost. Build ML architectures that work at Cardinal Health scale. Qualifications Required: 7+ years in the distribution or Healthcare industry and deep knowledge of their business practices. 3+ years of proven Machine Learning experience and involvement in packaged platform delivery and management. Strong working knowledge of a variety of machine learning techniques (Regression, Clustering, Decision Tree, Probability Networks, Neural Networks, Bayesian models etc.) Experience with Machine Learning and related technologies such as Python, Tensorflow, Torch, Amazon SageMaker, Jupiter Notebooks. Understanding of cloud platforms, cloud data engineering and integration concepts. Preferred: Delivery experience in Data Movement Technologies and Cloud Data Warehouse technologies including but not limited to experience with GCP, Big Query, Google Cloud Storage. Agile development skills and experience.

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