Senior Data Scientist, Condition Monitoring

Caterpillar•Chicago, IL

2d•$112,710 - $183,140

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. Join the Condition Monitoring Analytics team of Cat Digital and take a key role in guiding the development of advanced machine learning systems that assess the health and risk of our machines and components. As a senior contributor, you will help shape the technical direction for transforming telematics, environmental factors, and service history into actionable insights - helping customers prevent unplanned downtime and maximize productivity.

Requirements

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; ability to develop, implement and deliver related systems, products and services.
Programming Languages: Extensive knowledge of basic concepts and capabilities applying Python (NumPy, SciPy, Pandas, PyTorch, etc.) programming; ability to use tools, techniques and platforms to write and modify programming languages.
Query and Database Access Tools: Working 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 Master’s 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
Experience with advanced statistical methods such as survival analysis/reliability engineering, statistical process control, Bayesian inference, and time series analysis.
Practical applications of machine learning techniques such as Clustering, Regression/Classification, Random Forests, and Deep Learning.
Experience with model interpretability methods and communicating model behavior to stakeholders.
Competency in coaching and mentoring junior Data Scientists in the creation, validation, and application of statistical models as well as in implementing digital solutions.
Extensive experience applying python (NumPy, SciPy, pandas, etc) programming to solve business challenges (typically 3+ years).
Practical knowledge of cloud computing and workflow orchestration (e.g., Airflow) for building solutions in cloud environments (AWS, Snowflake, etc.).
Good communication, interpersonal, and collaboration skills.
Strong initiative to research and apply modern AI advancements, including generative approaches, to further enhance the evolution of predictive maintenance capabilities.

Responsibilities

Directing the data gathering, data mining, and data processing processes in huge volume; creating appropriate data models to support predictive maintenance.
Exploring, promoting, and implementing semantic data capabilities and advanced modeling paradigms to synthesize equipment insights from unstructured service data.
Defining requirements and scope of data analyses; presenting and reporting possible business insights to management using data visualization technologies.
Conducting research on data model optimization and evaluating emerging generative technologies to improve the effectiveness and accuracy of equipment health assessments.