Advanced degree (MS or PhD) in statistics, computer science, data science, mathematics, analytics, engineering, or related fields; experience applying advanced statistical concepts including sampling considerations, bias detection, weighting techniques, handling missing or outlier data, exploratory analysis, and longitudinal forecasting; and understanding of the data analytics lifecycle (e.g., CRISP-DM). Lead Data Science Initiatives: Dive deep into complex and often nebulous requirements, applying expertise in areas such as time series forecasting and Natural Language Processing (NLP) to build, train, and maintain predictive models.