Lead Data Scientist

Caterpillar Inc.•Nashville, TN

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. The Lead Data Scientist will be a technical expert, working in a team environment, to support the development, integration & enhancement for Caterpillar in Advanced Analytics and GenAI initiatives for the Technology & Analytics team. Significant responsibilities of this position are collaborating with people; enhancing the team’s creativity; maintaining knowledge of approaches used in similar projects; and pushing the technical bounds of experimentation while meeting customer commitments. The Lead Data Scientist acts as a technical leader for establishing and maintaining a sound analytic approach for solving the problem at hand. This individual will develop good networks within the technical community to enable them to collaborate on technical solutions, obtain resources and cooperation needed, and remove roadblocks so that they can ensure the success of their team.

Requirements

Business Statistics: 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.
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 of applying Python programming to solve business challenges; ability to use tools, techniques, and platforms to write and modify programming languages.
Database Management and Consumption: Extensive knowledge of data management systems; ability to use, support and access facilities for searching, extracting and formatting data for further use.
Data Analysis: Posses the ability to conduct thorough data analysis and produce data mapping documents per the business requirements; extensive capabilities in SQL, data modeling, and understanding of data processing including ETL / ELT.
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

Extensive experience applying Python (NumPy, SciPy, pandas, etc.) programming to solve business challenges.
Extensive experience with advanced data analysis and statistical methods such as regression, hypothesis testing, ANOVA, statistical process control, etc.
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.
In-depth technical and critical thinking skills and evidence of continuous learning in the analytics field.
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.
Working knowledge with cloud technologies (AWS, Azure, Google Cloud, etc.).
Experience with Snowflake and SQL
Experience with GenAI is plus.

Responsibilities

Conduct Data discovery, data preparation and data processing for business intelligence and ML / AI models.
Exploring, promoting, and implementing semantic data capabilities through data analytics and machine learning techniques.
Leading to define requirements and scope of data analyses, presenting and reporting business insights to management using data visualization technologies.
Conducting research on data model optimization and algorithms to improve effectiveness and accuracy on data analyses.
Plan technical deliverables and definition of done for each sprint for the Jr. Data Scientists/Engineers.
Communicate and Present the Technical Deliverables to stakeholders and business in an easy-to-understand way.
Demonstrate a breadth of knowledge in the application statistical methods and/or digital methods to solve business problems.
Develop, validate, train and implement statistical models, and implement digital solutions.
Participate on 3-4 projects/products concurrently.
Demonstrate strong initiative to research and apply new methods to exceed customer expectations.
Have a strong focus on continual learning in the Analytics field.
Possess thorough statistical and/or digital technology knowledge and the ability to solve low to medium complexity problems.
Have very good communication skills, being able to explain conclusions to customers with limited knowledge and experience with quantitative analytical methods.