Cummins
From Cummins's careers page
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Solves complex analytical problems using quantitative approaches through a combination of analytical, mathematical and technical skills. Researches, designs, implements and validates complex algorithms to analyze diverse sources of data to achieve targeted outcomes by leveraging complex statistical and predictive modeling concepts.
Participates in projects to support key objectives and business goals through the use of data science methodology. Leverages data science methodology to solve complex business problems. Creates multiple algorithms using complex statistical methodologies through the use of statistical programming languages and tools. Partners with domain experts to verify model capabilities. Partners with Solution Architect to enable appropriate data flow/data model, development using appropriate tools/technology, rapid prototyping and informs the design of analytical products. Partners with less experienced employees on data science tools and methodologies. Clearly articulates results, methodologies and learnings to stakeholder and peer group. Continuous development and advancement of the team through knowledge sharing and collaboration.
Collaborates - Building partnerships and working collaboratively with others to meet shared objectives.
Customer focus - Building strong customer relationships and delivering customer-centric solutions.
Decision quality - Making good and timely decisions that keep the organization moving forward.
Manages complexity - Making sense of complex, high quantity, and sometimes contradictory information to effectively solve problems.
Tech savvy - Anticipating and adopting innovations in business-building digital and technology applications.
Data Mining - Extracts insights from data by identifying relationships and patterns through use of a suite of data exploration and data visualization techniques to understand the underlying structure of the data and enable sound conclusions upon model building.
Predictive Modeling - Develops analytical or machine learning models by using appropriate variable transformations, feature selection strategies, imputation strategies, class rebalancing, resampling strategies and quality control measures to generate predictive insights used in solving business questions.
Programming - Creates, writes and tests computer code, test scripts, and build scripts using algorithmic analysis and design, industry standards and tools, version control, and build and test automation to meet business, technical, security, governance and compliance requirements.
Requirements Analysis - Evaluates relationships and interdependencies between requirements based upon their complexity and value to the business in order to determine feasibility and prioritization.
Statistical Modeling - Develops descriptive and explanatory statistical models, and simulations for regression, classification, outlier detection, anomaly detection, time series forecasting using knowledge of foundational statistics such as null hypotheses significance tests, regression models, generalized linear modeling, time series analysis, rank statistics, probability distribution fitting survival analysis, etc. to validate hypotheses for any given statistical or business question.
Problem Solving - Solves problems and may mentor others on effective problem solving by using a systematic analysis process by leveraging industry standard methodologies to create problem traceability and protect the customer; determines the assignable cause; implements robust, data-based solutions; identifies the systemic root causes and ensures actions to prevent problem reoccurrence are implemented.
Values differences - Recognizing the value that different perspectives and cultures bring to an organization.
College, university, or equivalent degree in relevant technical discipline, or relevant equivalent experience required. This position may require licensing for compliance with export controls or sanctions regulations.
Intermediate experience in a relevant discipline area is required with a demonstrated track record of analyzing complex business systems and large data sets. Knowledge of the latest technologies and trends in data science is highly preferred and includes:
Familiarity analyzing complex business systems, industry requirements, and/or data regulations
Background in processing and managing large data sets
Applied knowledge of big data, open source and third party toolsets
SQL query language
Clustered compute cloud-based implementation experience
Implementing Big Data platform solutions using open source and third-party tools
Microsoft Azure and/or Amazon Web services environment
Familiarity with validation and testing of machine learning systems
Familiarity with Continuous Integration and Continuous Delivery (CI/CD)
Core Responsibilities Unique to the Role Lead AI, Generative AI, and advanced analytics innovation initiatives by researching, evaluating, prototyping, and industrializing emerging AI capabilities that improve business outcomes, accelerate data product adoption, and create reusable enterprise assets across Supply Chain, Quality, Finance, and Engineering domains.
Drive AI-enabled productivity and development lifecycle acceleration by implementing intelligent automation, agentic AI, code generation, automated testing, knowledge retrieval, and AI-assisted decision support solutions that reduce software delivery effort, improve engineering productivity, and enhance operational effectiveness.
Develop scalable enterprise AI capabilities using Data Products as the foundation by partnering with Product Managers, Data Engineers, and Solution Engineers to leverage Snowflake, Databricks, enterprise data products, and cloud AI platforms to build production-ready AI solutions that can be reused across business functions.
Hands-on experience with modern AI and data platforms including Databricks, Snowflake, MLFlow, Azure OpenAI, Azure AI Services, vector databases, and scalable model deployment frameworks.
Strong understanding of modern AI solution patterns including Retrieval-Augmented Generation (RAG), semantic search, vector search, agentic AI, prompt engineering, model evaluation, fine-tuning approaches, and responsible AI practices.
Ability to identify high-value business opportunities and translate them into measurable outcomes including productivity gains, automation benefits, cycle-time reduction, cost optimization, improved decision-making, and enhanced customer experiences.
Bachelor’s degree in Data Science, Computer Science, Artificial Intelligence, Engineering, Mathematics, Statistics, or equivalent practical experience.
Experience building AI-powered engineering productivity solutions including developer copilots, intelligent assistants, automated testing frameworks, documentation generation, enterprise knowledge management platforms, and agentic workflow automation.
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