Job Summary:
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.
Key Responsibilities:
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.
Competencies:
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.
Education, Licenses, Certifications:
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.
Experience:
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
- Experience in building analytical solutions
Intermediate experiences in the following are preferred:
- Implementing Big Data platform solutions using open source and third-party tools
- Microsoft Azure and/or Amazon Web services environment
- Experience in Agile software development
- Familiarity with validation and testing of machine learning systems
- Familiarity with Continuous Integration and Continuous Delivery (CI/CD)
Core Responsibilities:
- Own end-to-end lifecycle of production-grade ML systems:From problem framing to modeling, deployment, monitoring, and retraining. Not just modeling.
- Drive AI productization with cross-functional influence:Translate business problems into deployable AI solutions; influence roadmap with Product, Engineering, and Architecture.
- Establish modeling and experimentation standards:Define best practices for model validation, drift monitoring, retraining triggers, and experimentation (A/B, offline vs online metrics).
Required Skills and Experience:
- Proven experience deploying models to production at scale(not notebooks but APIs, pipelines, batch/real-time inference)
- Strong Python + SQL + one cloud stack (Azure preferred)Must include ML pipelines, data engineering basics, and orchestration (e.g., Azure ML pipelines)
- Advanced ML problem-solving in ambiguous environmentsEvidence of solving business problems (not Kaggle-style tasks)
- Stakeholder-facing communication with measurable impactCandidate should show decisions driven by their models (e.g., revenue lift, cost reduction)
- Experience with LLMs / GenAI use cases(prompt engineering, RAG pipelines, evaluation frameworks)
Nice to have:
- MLOps maturity exposure(model monitoring, feature stores, CI/CD for ML)
Education and Certifications:
- College, university, or equivalent degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative discipline is required.
- Equivalent practical experience in Data science, Machine learning, or Advanced Analytics may be considered in lieu of formal education.
- Advanced degrees (Master’s or PhD) are not required but may be considered where supported by strong applied experience in solving business problems using Data Science techniques.
Job Systems/Information Technology
Organization Cummins Inc.
Role Category On-site with Flexibility
Job Type Exempt - Experienced
ReqID 2431343
Relocation Package No
100% On-Site No
