Senior Data Engineer & Data Scientist – Commercial Intelligence

International Motors, LLC•Lisle, IL

1d•$160,000 - $240,000•Hybrid

About The Position

International is on a bold journey to redefine commercial transportation. Evolving beyond our legacy as a truck and bus manufacturer, we are becoming a provider of integrated, sustainable transportation solutions powered by digital innovation, data, and AI. We are investing in the technologies, products, and talent that will shape the future of our industry. We have an exciting opportunity for a Senior Data Engineer & Data Scientist – Commercial Intelligence to help build the data and intelligence foundation behind Retail Sales Management (RSM), International’s digital sales platform supporting our dealer network. This is a highly hands-on technical role at the intersection of data engineering, data science, commercial strategy, and digital product development. You will create trusted, scalable commercial data products that bring together customer, prospect, dealer, vehicle, sales, service, parts, CRM, and other enterprise data and transform it into actionable intelligence embedded directly into sales workflows. The challenge goes beyond building pipelines and dashboards. You will help answer the questions that matter to a salesperson: Who should I engage next? Why? What opportunity exists? And what action is most likely to create value? We are looking for someone with a strong sense of ownership, curiosity, and a solution-oriented mindset: “we may not have done this before—let’s figure it out and learn quickly.” This is a full-time position be based in Lisle, Illinois Monday through Thursday with hybrid in-office and remote work flexibility on Friday

Requirements

Strong hands-on experience building production-grade data pipelines, data models, analytical products, and commercial intelligence solutions at enterprise scale.
Advanced SQL and Python skills with sound software-engineering practices.
Experience with modern cloud data platforms and distributed processing; Palantir Foundry/AIP and/or Databricks strongly preferred.
Experience developing, deploying, and monitoring machine-learning or statistical models against large, complex datasets.
Strong understanding of data quality, lineage, observability, governance, testing, and production operations.
Experience solving entity resolution, identity matching, customer-360, master-data, or similar commercial data problems.
Experience with customer, sales, CRM, marketing, vehicle, transactional, service, parts, or other commercial datasets.
Ability to translate ambiguous commercial problems into data products, models, experiments, and measurable outcomes.
Experience embedding analytical intelligence into operational applications and workflows.
Familiarity with LLMs, retrieval, AI agents, or AI-assisted decision systems.
Demonstrated ability to influence technical direction and drive outcomes across Product, Engineering, Data, and business teams.
Highly proactive, ownership-driven mindset with the ability to operate independently in ambiguity.
Excellent written and verbal communication skills.
Coach and mentoring of other data scientists and data engineers
Bachelor's degree and at least 9 years of data engineering, data quality and/or analytics or statistical analysis experience
At least 2 years of lead experience
OR
Master's degree and at least 5 years of data engineering, data quality and/or analytics or statistical analysis experience
At least 2 years of lead experience
OR
At least 12 years of data engineering, data quality and/or analytics or statistical analysis experience
At least 2 years of lead experience

Nice To Haves

Hands-on experience engineering and operationalizing enterprise-scale data and analytics solutions.
Experience leading technical work, defining solution approaches, and influencing architecture or data strategy across cross-functional teams.
Deep experience with Palantir Foundry, including data pipelines, ontology design, AIP, OSDK, and operational applications.
Experience with Databricks and modern lakehouse/data-engineering patterns.
Experience applying machine learning to sales, lead generation, customer intelligence, propensity, recommendation, or next-best-action use cases.
Experience with commercial vehicle, automotive, industrial B2B, dealer/distributor, or complex channel-sales environments.
Experience with API-first and event-driven architectures and integrating analytical data products into production applications.
Experience with domain-driven design, semantic modeling, master data, or commercial data domains.
Advanced degree in computer science, data science, statistics, engineering, analytics, or a related quantitative discipline.

Responsibilities

Design, build, and maintain scalable data pipelines and data products supporting RSM and broader commercial intelligence capabilities.
Integrate, normalize, and model customer, prospect, dealer, vehicle, sales, service, parts, CRM, digital interaction, and external data.
Develop robust approaches for identity resolution, entity matching, deduplication, normalization, lineage, and data quality across complex commercial datasets.
Build and operationalize analytical and machine-learning models for lead scoring, opportunity identification, customer segmentation, propensity, prioritization, and next-best-action recommendations.
Translate complex commercial data into actionable intelligence embedded directly into dealer and sales workflows—not simply reports and dashboards.
Partner with Product, Sales, Commercial, UX, Data, and Engineering teams to turn high-value business problems into scalable data and analytical solutions.
Design reusable commercial data models, semantic structures, and ontology concepts for customers, prospects, vehicles, dealers, opportunities, and sales activity.
Define and monitor data quality, model performance, and business-impact metrics and resolve issues affecting the trustworthiness of commercial intelligence.
Prototype and evaluate AI/ML approaches, including generative AI and LLM-based capabilities, that improve commercial decision-making and sales effectiveness.
Establish engineering and data-science practices covering testing, observability, documentation, governance, versioning, CI/CD, and production deployment.
Own what you build, including operational readiness, production support, incident response, quality standards, and technical lifecycle management.
Serve as a senior technical leader within the RSM product team, influencing architecture and technical direction while mentoring other practitioners.
Communicate complex analytical findings and technical decisions clearly to technical teams, product leaders, commercial stakeholders, and senior leadership.