Data Scientist II - AI & Risk Modeling

Key Skills

Analytical and Problem solving, AWS, Azure, Data science techniques, Data Visualization, Deep Learning, Design, Google Cloud Platform (GCP), Keras software library, KPI, Machine learning techniques, NumPy, Pandas, PySpark, PyTorch

Job Description

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.

The Cat® Digital group is the digital and technology arm of Caterpillar Inc., responsible for bringing world class capabilities to our products and services. With over 1.4 million connected assets worldwide, we're focused on using data, advanced analytics, and AI capabilities to help our customers build a better world. To accomplish this, we’re deploying analytics that generate insights, recommend optimized decisions, and improve products by intelligently integrating massive quantities of telematics information, transactional records, images, unstructured documents, and other data sources. 

Join our group of world-class data scientists and apply deep learning to remote sensor data to diagnose potential issues and schedule proactive repairs before failures arise. Or improve products and product recommendations by developing a detailed understanding of how customers use their Cat equipment. Or use the insights gained from equipment and customer profiles to design customized service offerings that maximize our customers’ value of their Cat equipment fleets. You’ll have significant opportunity to do meaningful work that makes a real impact.

JOB DUTIES: 

The Aftermarket Analytics Team within Cat Digital is looking for a talented and motivated Data Scientist II to develop equipment condition monitoring models to power scalable aftermarket services solutions for customers and dealers. You will use machine learning, statistics-based, and physics-based analytics techniques on time-series sensor data, machine fault codes, inspections and analysis records, and other datasets to identify health anomalies, predict equipment failure modes, build equipment risk models, and estimate remaining useful life. Excellent python coding skills are required. Familiarity with condition monitoring, or related modeling and analysis of heavy equipment engineering systems, is a plus.

The principal responsibility of the Data Scientist II is to be an independent contributor to multi-person analytic teams. This position has a depth of knowledge in quantitative analytic methods, data management, and or associated digital technologies suitable to handle all but the most complex issues. The Data Scientist II is expected to be familiar with the company’s processes, products, and organization, as well as its customers, competitors, and stakeholders. Decisions on routine, medium risk issues that may affect the project team, suppliers or internal customers may be made by this position. Challenges include meeting expectations in delivering results, learning to refine solutions to better fit complex situations, making timely decisions, and communicating effectively with all project stakeholders. The Data Scientist II also mentors and develops the capabilities and organizational knowledge of more junior data scientists.

The Data Scientist II will prototype, develop, iterate, test, and deploy models to production under the direction of a team lead, successfully meeting program objectives/timelines and solving problems with appropriate supervision. A fast learner with excellent communication skills, the successful candidate can explain conclusions to customers who have limited knowledge of quantitative analytics methods and will collaborate effectively with diverse teams (including other data scientists, product design/simulation experts, platform/application developers, condition monitoring analysts) to develop analytics algorithms that enable condition monitoring commercial offerings of strategic importance.

The data scientist II will have a split responsibility for the first year between building world-class machine learning predictive models and defining, managing, and maintaining content in a customer facing application (analytic model understanding, baseline templates, data mapping & visualization). Other responsibilities include analytic model monitoring using cloud services (AWS preferred), developing, and implementing routines using Python and or associated technologies to monitor telematics data quality and maintain backend content for customer facing applications. The candidate should have a deep understanding of technologies supporting application development (specifically CLI, Postman API).

Example projects include:

• Applying machine learning to remote sensor data to diagnose potential issues and recommend proactive repairs before failure
• Developing major component remaining useful life models that enable adaptive overhaul intervals while minimizing risk

BASIC QUALIFICATIONS: 

• M.S. in data science, computer science, applied mathematics/statistics, engineering, physics, or similar field with quantitative coursework
• 2-3 years work experience in data science and/or software development 
• Strong python skills for data science (numpy, pandas, pyspark, pytorch, keras, etc.)

TOP CANDIDATE WILL ALSO HAVE: 

• Demonstrated practical application of advanced statistical methods, machine learning, and deep learning to solve technical or business challenges
• Familiarity with condition monitoring or modeling/analysis of heavy equipment systems (automotive, aerospace, engine, transmission, construction, mining, industrial, etc.)
• Experience deploying production code in cloud environments (AWS, Azure, GCP)
• Strong technical and problem-solving skills, with evidence of continuous learning in data science
• Initiative to research and apply new methods and digital technologies to exceed customer expectations