Data scientist I, Machine Analytics
Employment Information
Your Work Shapes the World at Caterpillar Inc.
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 1.4 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.
Working with a Fortune 100 leader, you can build your career on a global scale and take advantage of development opportunities with emerging technologies. We’ve created an inclusive environment for you to explore your passions, make an impact and do the work that really matters. Join Us.
Job Purpose:
The Equipment Usage Team within Aftermarket Analytics is seeking a talented and motivated Data Scientist to work in aftermarket analytics, product condition monitoring analytics, service, maintenance, and other strategic analytics areas of focus.
Our analytic products will determine usage insights that help our customers work safer and more productively and with less downtime, while helping our enterprise understand the market trends that shape our future business. This role will require a skillset in advanced statistical modeling, data science, pipeline development and scalability, as well as domain knowledge of construction, mining, and industrial equipment.
The Data Scientist will demonstrate an expanded breadth of knowledge and the ability to handle issues independently. They also demonstrate solid communication skills; planning and organization, teamwork, and decision-making skills; a strong concern and sense of urgency for customers; a strong focus on continual learning in the Analytics field; and good knowledge of Caterpillar Inc., and its products and services.
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 analyses; 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.
Skills You will Have:
Business Statistics: Knowledge of the 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. Level Working Knowledge:
- Explains the basic decision process associated with specific statistics.
- Works with basic statistical functions on a spreadsheet or a calculator.
- Explains reasons for common statistical errors, misinterpretations, and misrepresentations.
- Describes characteristics of sample size, normal distributions, and standard deviation.
- Generates and interprets basic statistical data.
Analytical Thinking: 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. Level Working Knowledge:
- Approaches a situation or problem by defining the problem or issue and determining its significance.
- Makes a systematic comparison of two or more alternative solutions.
- Uses flow charts, Pareto charts, fish diagrams, etc. to disclose meaningful data patterns.
- Identifies the major forces, events and people impacting and impacted by the situation at hand.
- Uses logic and intuition to make inferences about the meaning of the data and arrive at conclusions.
Machine Learning: Knowledge of principles, technologies and algorithms of machine learning; ability to develop, implement and deliver related systems, products and services. Level Working Knowledge:
- Explains the definition and objectives of machine learning.
- Describes the algorithms and logic of machine learning.
- Distinguishes between machine learning and deep learning.
- Gives several examples on the implementation of machine learning.
Programming Languages: Knowledge of basic concepts and capabilities of programming; ability to use tools, techniques and platforms in order to write and modify programming languages. Level Extensive Knowledge:
- Describes the basic concepts of programming and program construction activities.
- Uses programming documentation including program specifications in order to maintain standards.
- Describes the capabilities of major programming languages.
- Identifies locally relevant programming tools.
Query and Database Access Tools: Knowledge of data management systems; ability to use, support and access facilities for searching, extracting and formatting data for further use. Level Working Knowledge:
- Defines, creates and tests simple queries by using associated command language in a specific environment.
- Applies appropriate query tools used to connect to the data warehouse.
- Obtains and analyzes query access path information and query results.
- Employs tested query statements to retrieve, insert, update and delete information.
- Works with advanced features and functions including sorting, filtering and making simple calculations.
Top Candidates will also have:
- Experience with advanced data analysis and statistical methods such as regression, hypothesis testing, ANOVA, statistical process control, etc.
- Practical applications of machine learning techniques such as Clustering, Logistic Regression, CART, Random Forests, SVM and Neural Networks.
- Work experience in the industries of heavy and industrial equipment (ex. automotive, aerospace, engine, transmission, construction, mining, industrial, etc.)
- In-depth technical and problem-solving skills and evidence of continuous learning in the analytics field
- Advanced experience using Python data science packages (NumPy, SciPy, pandas, etc.) to solve business challenges.
Visa sponsorship available for eligible applicants. Any offer of employment is conditioned upon the successful completion of a drug screen.
EEO/AA Employer. All qualified individuals - Including minorities, females, veterans and individuals with disabilities - are encouraged to apply.
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