Senior Data Scientist

Caterpillar•Chicago, IL

15h•Onsite

About The Position

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. Role Summary: Leads the design and delivery of advanced analytics, machine learning, and data quality solutions on large-scale enterprise datasets to enable data-driven decision-making, operational efficiency, and trusted customer/asset insights across the business. Plays a critical role in translating ambiguous business problems into scalable, production-ready data and ML solutions.

Requirements

Business & Applied Statistics – Working to Advanced Knowledge
Applies statistical methods to real-world business problems (e.g., entity resolution confidence scoring, model evaluation).
Interprets results and translates findings into actionable recommendations.
Identifies data biases, sampling issues, and potential misinterpretations.
Accuracy & Data Quality – Extensive Experience
Champions high-quality, production-grade data solutions.
Designs validation frameworks, automated checks, and monitoring for pipelines.
Balances speed and accuracy in fast-moving delivery environments.
Drives improvements in enterprise data quality standards and practices.
Analytical Thinking – Advanced Knowledge
Breaks down ambiguous, complex problems into structured analytical approaches.
Identifies root causes in data inconsistencies, customer duplication, and system gaps.
Evaluates multiple solution paths, balancing business impact, scalability, and effort.
Synthesizes insights into clear recommendations for leadership.
Machine Learning & AI – Advanced Knowledge
Develops and deploys machine learning models (e.g., clustering, classification, entity resolution, NLP).
Applies Python-based ML ecosystems (e.g., pandas, scikit-learn, NLP libraries).
Designs experiments, evaluates model performance, and iterates based on results.
Understands tradeoffs between model complexity, interpretability, and scalability.
Programming & Software Practices – Advanced Working Knowledge
Writes clean, efficient, and production-ready code (primarily Python, SQL).
Implement version control, testing, and modular design principles.
Contributes to shared codebases and reusable components.
Query & Data Engineering (SQL / Snowflake) – Extensive Experience
Designs and optimizes complex queries across large-scale, distributed datasets.
Builds performant data pipelines, transformations, and aggregation layers.
Applies advanced SQL (window functions, CTEs, multi-source joins).
Optimizes cost and performance in cloud data platforms (Snowflake).
Requirements & Stakeholder Engagement – Advanced Working Knowledge
Partners with business stakeholders to define clear problem statements and success criteria.
Translates business needs into analytical approaches and technical requirements.
Communicates progress, risks, and tradeoffs effectively.

Nice To Haves

Top Candidate Will Have:

Responsibilities

Lead end-to-end analytics and ML solutions
Own problem framing, data exploration, feature engineering, model development, validation, and deployment.
Deliver scalable solutions in Snowflake / cloud environments aligned with enterprise standards.
Drive data quality and customer/entity resolution initiatives
Design and implement approaches for customer de-duplication, entity resolution, and master data improvement.
Partner with upstream/downstream teams to improve data accuracy, consistency, and usability.
Develop and operationalize machine learning capabilities
Apply NLP, entity matching, and advanced analytics techniques to improve business processes.
Move models from experimentation to production-grade pipelines with monitoring and performance tracking.
Translate analytics into business impact
Collaborate with product owners, stakeholders, and leadership to define use cases and success metrics.
Present insights in a clear, outcome-focused manner to drive adoption and decision-making.
Mentor and elevate team capabilities
Provide technical guidance to junior team members within Sulguni.
Promote best practices in coding, data modeling, and experimentation.
Continuously improving tools, methods, and scalability
Research and implement improvements in algorithms, data models, and processing approaches.
Optimize performance, cost, and reliability of data and ML pipelines.