Lead Data Scientist: Aftermarket Analytics & AI

Caterpillar Financial Services Corporation; Champaign (IL); On-site; USD 128,000 - 209,000; Full time

Benefits offered by this job

Medical, dental, and vision benefits

401(k) savings plans

Paid time off

Job summary

Caterpillar Inc. is seeking a Lead Aftermarket Data Scientist to enable services growth within Cat Digital's global aftermarket analytics team.

You will leverage advanced analytics on data from 1.5M assets to maximize value of digital products and services and collaborate with aftermarket leads, insights, and business segments. You will apply machine learning and statistical techniques to drive actionable insights, build predictive models, and communicate results with stakeholders across the

Qualifications

• Advanced knowledge of statistical methods and ML techniques.
• Experience taking a project from PoC to production.
• Experience with data from multiple domains and providing forecasts.
• Strong programming and data visualization skills.

Responsibilities

• Participate on multiple projects exploring advanced statistical methods to solve business problems.
• Own assigned projects and drive results with minimal supervision.
• Prioritize opportunities and collaborate with cross-functional teams.
• Use Aftermarket data to develop analytical solutions for business needs.
• Create compelling messaging and presentations to communicate results.

Skills

Python

NumPy

Pandas

SciPy

Machine Learning

Statistics

GitHub

Clouds (AWS/Azure/GC)

Data Analysis

Communication

Education

Master's or PhD in data science, statistics, mathematics

Tools

AWS

Azure

Google Cloud

Job description

Caterpillar Inc. is seeking a Lead Aftermarket Data Scientist to enable services growth within Cat Digital's global aftermarket analytics team.

You will leverage advanced analytics on data from 1.5M assets to maximize value of digital products and services and collaborate with aftermarket leads, insights, and business segments. You will apply machine learning and statistical techniques to drive actionable insights, build predictive models, and communicate results with stakeholders across the