The Lead Data Scientist is part of the Enterprise Advanced Analytics & AI team within the Enterprise Data & Insights organization. The Lead Data Scientist will play a pivotal role in data discovery, connecting datasets, data wrangling, building models, and model deployment for the R&D organization. This position is an excellent opportunity for an individual to contribute to the digital initiatives and data science projects supporting R&D. The Lead Data Scientist will focus on staying up to date with available modeling and analytical techniques and ensuring the appropriate techniques are applied to R&D initiatives. The R&D Data Scientist will utilize a combination of IT skills, data skills, analytics skills, and chemistry subject matter expertise. Role will engage and have regular discussions with other data scientists, data analysts, chemists, scientists, and internal customers to successfully move a given project forward. Essential Functions Problem Analysis and Project Management Collaborate with chemists and material scientists on methods and processes to create and manage experimental results using FAIR data principles. Participate in establishing the technical approach for integrating scientific knowledge, formulation science, and machine learning methods to accelerate the development of coatings. Lead project discovery through requirements gathering, analysis, design documentation, and impact analysis for model design. Understand business needs, determine data/model usage goals, and create project plans. Plan and organize tasks, report progress, and coordinate with other team members. Identify opportunities to create data-driven ML models in R&D. Identify, lead the implementation of, and validate appropriate statistical/ML models for specific projects in the R&D organization. Data Exploration and Preparation Apply statistical analysis, machine learning, and visualization techniques to various types of data. Test hypotheses using various quantitative methods. Display drive and curiosity to understand the business process to its core. Network with R&D experts to better understand the mechanics that generate data in R&D. Network with external functional areas to connect and join lab generated data to enterprise data sets. Perform data discovery and wrangling to run models utilizing experience in data extraction and data pre-processing and manipulation. Machine Learning Apply various ML and advanced analytics techniques to perform classification or prediction tasks. Apply chemical and materials domain knowledge to develop models that accelerate the development of new formulations. Testing of ML models, such as cross-validation and new data collection. Keep team appraised of developments in machine learning/AI/statistical research literature that may be of practical use in R&D. Design and Deployment Develop, debug, refine, deploy, and maintain analytical models using Python (including SimPy, SciPy, SciKit, RDKit, NumPy, and other data science and data visualization libraries in Python), R, and other software development and data science tools, including maintaining and updating existing models. Develop, deploy, and maintain visualizations and interactive reporting/analytics tools for analytical models using Python, Tableau, Visual Components, a[SC1] nd other data visualization tools. Coach peers on advanced statistical and ML techniques. Other Train and mentor other R&D staff on data science principles and techniques. Train peers on specialist data science topics. Network with internal and external partners. Upskill yourself (through conferences, publications, courses, local academia, and meetups). Promote collaboration with other teams within the organization. Encourage reuse of artifacts.