Data Scientist – Customer AnalyticsThe primary responsibilities of this role are to: Deliver best-in-class analytics solutions for the customer-centric commercial operations of the company. Topics include but are not limited to customer analytics, segmentation analysis, predictive cross-sell, churn prediction, and agronomic recommendation. Results will drive business decisions and significant revenue. Develop practical, interpretable, and automated solutions for these business problems through expertise in feature engineering, statistical analysis, predictive modeling (classification and regression), simulation, and optimization methodology by working in a highly interactive, team-oriented environment. Write model documentation to detail problem formulation, modeling approach, validation, data requirements, and implementation steps. Support stakeholder requests for model development, implementation, and feature improvements, and be able to communicate results to a broad range of audiences in a clear, interpretable manner. Work closely with data science teams and business partners to break down complex business problems, and gather requirements to build feasible solutions that are impactful, accurate, and validated. Collaborate with system integration and data warehouse engineers on data extraction and data cleaning (ETL). Be creative, resourceful, detail-oriented, able to take initiative, and exercise critical thinking when it comes to problem-solving. Be a proactive member of a global data science team to adopt and advocate for coding best practices and documentation.Required qualifications include a Bachelor’s degree with at least 4+ years of experience, Master’s degree with 2+ years of experience, or a Ph.D. in one of the following areas: Data Science, Economics, Electrical/Industrial Engineering, Applied Mathematics, Statistics, Computer Science, or another related quantitative discipline.
Experience conducting statistical inferences, time-series analysis, statistical simulation, analysis, and building machine learning models using disparate sources of data. Advanced programming skills in R or Python or both employing object-oriented programming, good programming practices, use of IDEs, adherence to code style and good documentation for machine-learning tasks, and working knowledge of version-control tools (such as git and git workflows including CLI). Excellent work ethic, and a strong desire to learn new skills for technical and professional growth.