Senior Data Scientist

Fuse is on a mission to disrupt healthcare. Our focus on technology, design and a product mindset will help drive that disruption and establish Cardinal Health as a leader in healthcare technology. Fuses innovation culture and modern product development teams power the evolution of our commercial products and spark the creation of new businesses and products. We leverage technology and insights across the enterprise to solve customer problems and forge new models. Design thinking drives our teams to imagine and invent the next horizon. Its more than technology innovation its creating new business models beyond our wildest dreams. With a strong leadership team setting the bar high, Fusers are challenged to bring their creativity, technical expertise and diverse perspectives to do the best work of their lives.

The Data Scientist is a key role in the transformation of business. They will work closely with business stakeholders to understand their goals and determine how data science can be used to achieve those goals. They will use relevant statistical techniques, machine learning models and artificial intelligence algorithms to analyze data, design data models and derive insights to influence actions that maximize business value and effectiveness for the commercial technology segments.

*_What Data Science contributes to Cardinal Health_*

The Data & Analytics Function oversees the analytics life-cycle in order to identify, analyze and present relevant insights that drive business decisions and anticipate opportunities to achieve a competitive advantage. This function manages analytic data platforms, the access, design and implementation of reporting/business intelligence solutions, and the application of advanced quantitative modeling.

Data Science applies base, scientific methodologies from various disciplines, techniques and tools that extracts knowledge and insight from data to solve complex business problems on large data sets, integrating multiple systems.

*Responsibilities*

+ Designing and developing machine learning and deep learning solutions and systems.

+ Using statistical analysis determine which?data modeling?approach to use and running / training machine learning tests and experiments. Implementing appropriate ML algorithms.

+ Mine and analyze large structured and unstructured datasets. Identify the data attributes that influence the outcome, define, and monitor metrics, create data narratives, and builds tools to drive decisions.

+ Lead data science projects and partner with cross functional teams to deliver end-to-end advanced analytics/machine learning solution.

+ Propose, own initiatives and demonstrate though leadership that improve the efficiency and productivity of the data science Center of Excellence

+ Work across diverse teams, perspectives and opinions and quickly build consensus.

+ Encourages informed risk-taking and acts as a catalyst for innovation at Fuse; generates practical, sustainable and creative options to solve problems and create business opportunities, while maximizing existing resources.

+ Communicate results and statistical concepts to key business leaders.

+ Create visualizations of data that make distributions, trends, and results easy to understand for business leaders.

+ Diagnose business need, analyze business process, data flows, IT / technical artifacts and extract understanding of how the system or business process works as input into projects / solutions.

+ Proactively develops, maintains and evangelizes technical knowledge in Machine Learning and adjacent technologies.? Keeps up-to-date on current trends and best practices.

+ Performs assessments and listens to internal/external customers to understand and anticipate their needs and determine their priorities in the context of the overall enterprise.

+ Possess deep functional and technical understanding of the Machine Learning technologies (Googles Cloud Platform, custom and COTS-embedded) and provide prescriptive guidance on how these are leveraged across the Fuse landscape.

+ Identify high-value ML business opportunities and work with Business and IT stakeholders to realize business benefit.

+ Assist in implementation of ML related efforts to ensure their operational viability.

+ Assist skill uplift across the Fuse and the business.

+ Understand and enable Commercial Technologys business via an efficient and effective ML practice that provides ML solutions and outcomes at the most efficient cost.?

+ Ensure effective designs and quality deliverables in the ML space by designing and developing machine learning and deep learning systems, running machine learning tests and experiments, implementing appropriate ML algorithms

+ Aide adoption of ML solutions within the business by conducting training, coordinating demos.

+ Support and influence delivery operations within platforms by continually looking for opportunities to streamline delivery, increase business value and measure platform value via metrics and measures.

+ Weigh Risk & ROI against schedule & budget considerations and provides solution recommendations.

+ Ensure projects are delivered in-line with ML Reference Architecture, road map and to the defined standards and best practices.

+ Effectively communicate and influence others towards future state solutions and architectures.? Evangelize the purpose and value of Machine Learning solutions.

*_Qualifications_*

+ Deep knowledge of clinical domain and datasets

+ Experience in Natural language processing (NLP), Prompt engineering, Embedding, Vector DB

+ Implement, train, and fine-tune LLM and GPT-like models on large-scale datasets to ensure optimal performance and accuracy

+ Proven Machine Learning experience and involvement in data science projects and product delivery.

+ Experience in the Design and implementation of AI/ML models and solutions using Google Cloud Platform technologies, including but not limited to Vertex AI , BigQuery, AI Platform, TensorFlow Extended (TFX), Dataflow, and Compute Engine.

+ Experience with Machine Learning and related technologies such as Python, other relevant libraries and frameworks like PyTorch and tools like Jupiter Notebooks, git.

+ Strong understanding of machine learning principles, including supervised and unsupervised learning, deep learning, and reinforcement learning. Knowledge of generative models and autoregressive models.

+ Understanding of cloud data engineering and integration concepts.

+ Strong mathematical and statistical skills.

+ Understanding of exploratory data analysis, descriptive analytics, prescriptive analytics, predictive analytics and so on.

+ 10+ years in the Healthcare industry and knowledge of clinical data preferred.

+ MD, PharmD by training and Healthcare informatics experience preferred

+ Delivery experience in Data Engineering with Google Cloud Platform including Big Query, Dataflow, Cloud Data Fusion, Cloud Composer, Dataproc, Cloud Pubsub, Cloud Function etc.

+ Delivery of related information software solutions such as data warehouses and integration.