Data Scientist Ii- Data Algorithms & Optimization, Cat Digital

Caterpillar Caterpillar · Industrial · Chicago, IL +1

Data Scientist II role at Caterpillar Digital focused on delivering advanced analytics, data science, and machine learning solutions for digital products and enterprise initiatives. Responsibilities include developing analytical models, analyzing data, supporting data quality, and communicating insights. Requires knowledge of business statistics, analytical thinking, machine learning, Python, and requirements analysis. Experience with generative AI and cloud technologies is a plus.

What you'd actually do

  1. Design, develop, and maintain advanced analytical models, including machine learning and statistical approaches, to support digital products and enterprise initiatives.
  2. Analyze large, complex datasets to identify trends, patterns, and opportunities for improvement.
  3. Partner with cross-functional teams to define analytical requirements and translate business questions into data science solutions.
  4. Support data quality initiatives by validating data sources, identifying gaps, and recommending improvements.
  5. Communicate insights and model results clearly to technical and non-technical stakeholders.

Skills

Required

  • Business Statistics
  • Analytical Thinking
  • Machine Learning
  • Programming Languages (Python)
  • Requirements Analysis

Nice to have

  • Bachelors, Masters, or PhD degree in Applied Statistics, Data Science, Business Analytics, Predictive Analytics, Business Intelligence & Analytics, Mathematics, Computer Science, Engineering
  • 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, Random Forests, SVM or Neural Networks
  • working with generative AI, with an understanding of strengths, limitations, and responsible use considerations
  • quantifying the costs, benefits, risks and chances for success before recommending a course of action
  • cloud technologies (AWS, Azure, Google Cloud, etc.)
  • version control / repositories such as GitHub
  • Agile environment

What the JD emphasized

  • extensive knowledge of the statistical tools, processes, and practices to describe business results in measurable scales
  • Extensive knowledge of principles, technologies and algorithms of machine learning
  • Working knowledge of basic concepts and capabilities of applying Python programming to solve business challenges
  • working with generative AI, with an understanding of strengths, limitations, and responsible use considerations

Other signals

  • advanced analytics
  • machine learning
  • data science
  • digital products
  • enterprise initiatives