Lead Data Scientist

Role Overview

• Provide specialized data science expertise and technical leadership to support all aspects of forecast analytics for Caterpillar aftermarket parts and related special projects.
• Manage and optimize key analytical processes that support the enterprise-critical Dealer Parts Orders (DPO) forecast and insights generation.
• Oversee the end-to-end Parts Long-Term Forecast (LTF) process – spanning system architecture design and implementation; data engineering and statistical model development; user training, and stakeholder approvals.
• Ensure accurate and efficient forecasting.
• Safeguard the integrity of both the Sales & Operations Planning (S&OP) forecast disaggregation and the annual business plan disaggregation for DPO.
• Own the statistical modeling required to calculate probabilistic confidence intervals for aftermarket parts forecasts.
• Ensure timely, accurate updates to DPO systems and provide expert-level support for all product-based allocation inquiries.
• Set priorities and prepare work plans to complete broadly defined assignments and achieve desired results.
• Responsible for Value Stream Mapping for Business Process analysis and improvement.

Requirements

• Doctoral or Master’s degree with 5 years of experience or a Bachelor’s degree with 10 years of experience in Computer Science, Mathematics, Engineering, Accounting, Statistics, Data Science, Business Analytics, or a closely related field with extensive coursework in mathematical and statistical modeling.
• 5+ years of extensive experience in applying statistical models and methods to solve a wide range of industry problems.
• 5+ years of extensive experience in applying statistical tools and techniques for time series analysis and modelling; demonstrating a deep understanding of fundamental concepts of time series analysis: stationarity; seasonality; time series decomposition.
• 5+ years of extensive experience in developing end-to-end analytics solutions that span from data pipeline to insights delivery.
• Deep expertise and experience in applying classical statistical modelling methods, tools, and techniques for time series forecasting included in the GLM, ARIMA and State Space family of models.
• Deep expertise and experience in contemporary deep learning tools and techniques for time series forecasting included in Transformer family of models with self-attention mechanism.
• Deep understanding of models and methods for Hierarchical time series and forecast reconciliation.
• Extensive experience and proficiency in R, Python and SQL / Snowflake queries.
• Experience with AWS Cloud platform and AWS Glue.
• Experience with Alteryx and SAS.
• Extensive experience with PowerBI for development of reporting dashboards.
• Experience with Rshiny / Dash / Streamlit for development of interactive web applications for forecast analytics.
• High level of interpersonal skills and excellent communication and storytelling skills.
• Experience in Value Stream Mapping for Business Process analysis and improvement.

Tech Stack

• AWS
• Cloud
• Python
• SQL

Benefits

• Medical, dental, and vision benefits*
• Paid time off plan (Vacation, Holidays, Volunteer, etc.)*
• 401(k) savings plans*
• Health Savings Account (HSA)*
• Flexible Spending Accounts (FSAs)*
• Health Lifestyle Programs*
• Employee Assistance Program*
• Voluntary Benefits and Employee Discounts*
• Career Development*
• Incentive bonus*
• Disability benefits
• Life Insurance
• Parental leave
• Adoption benefits
• Tuition Reimbursement