Lead Data Scientist, Cat Foresight

Caterpillar•Chicago, IL

12d•$126,000 - $204,720

About The Position

When you join Caterpillar, you're joining a global team who cares not just about the work we do – but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here – we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it. Cat Digital is the digital and technology arm of Caterpillar Inc., leveraging the latest technologies to build industry leading digital solutions for our customers and dealers. With over 1.5 million connected assets worldwide, our teams use data, technology, advanced analytics, telematics, and AI capabilities to help our customers build a better, more sustainable world. Job Summary: We are seeking a Lead Data Scientist to act as a player/coach, guiding a high-performing team in building the next generation of intelligent analytics for Cat Foresight. This role combines hands-on technical expertise with leadership responsibilities, driving innovation in condition monitoring with predictive and attentional models that enable advanced insights and efficient decision-making.

Requirements

Business Statistics: Extensive experience with statistical tools, processes, and practices to describe business results in measurable scales; ability to use statistical tools and processes to assist in making business decisions.
Accuracy and Attention to Detail: Extensive experience with the necessity and value of accuracy; ability to complete tasks with high levels of precision.
Analytical Thinking: Extensive knowledge of techniques and tools that promote effective analysis; ability to determine the root cause of organizational problems and create alternative solutions that resolve these problems.
Machine Learning: Extensive knowledge of principles, technologies and algorithms of machine learning (particularly recommendation/attentional models); ability to develop, implement and deliver related systems, products and services.
Programming Languages: Extensive knowledge of basic concepts and capabilities of applying Python programming to solve business challenges; ability to use tools, techniques and platforms in order to write and modify programming languages.
Query and Database Access Tools: Extensive knowledge of data management systems; ability to use, support and access facilities for searching, extracting and formatting data for further use.
Requirements Analysis: Working knowledge of tools, methods, and techniques of requirement analysis; ability to elicit, analyze and record required business functionality and non-functionality requirements to ensure the success of a system or software development project.

Nice To Haves

Typically, a Masters or PhD degree in Applied Statistics, Data Science, Business Analytics, Predictive Analytics, Business Intelligence & Analytics, Mathematics, Computer Science, Engineering (Aerospace, Electrical, Mechanical, Computer, Industrial, Agricultural, etc.), or equivalent technical degree
Extensive experience applying Python (NumPy, SciPy, pandas, etc.) programming to solve business challenges.
Extensive experience in practical applications of Machine Learning techniques such as Clustering, Logistic Regression, Random Forests, SVM or Neural Networks.
Advanced experience in quantifying the costs, benefits, risks and chances for success before recommending a course of action.
Working experience with heavy equipment engineering and/or condition monitoring.
In-depth technical and problem-solving skills and evidence of continuous learning in the analytics field
Must demonstrate strong initiative, interpersonal skills, and the ability to communicate effectively.

Responsibilities

Directing the data gathering, data mining, and data processing processes in huge volume; creating appropriate data models.
Exploring, promoting, and implementing semantic data capabilities through Natural Language Processing, text analysis and machine learning techniques.
Leading to define requirements and scope of data analysis; presenting and reporting possible business insights to management using data visualization technologies.
Conducting research on data model optimization and algorithms to improve effectiveness and accuracy on data analyses.