Interested in this Data Scientist role at Lubrizol?
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Shape the Future with Us.
At Lubrizol, we’re bringing to life the chemistry behind clean water, efficient transportation, reliable infrastructure, critical medicines, and the products people rely on every day through science, sustainability, and a culture of inclusion.
As part of our global team, you’ll be empowered to make a real impact - on your career, your community, and the world around you.
Location: Wickliffe, OH
Job Type: Full-time / On-site
How You’ll Make an Impact
As a Senior Data Scientist, AI & Product Development, you'll play a pivotal role in shaping the future of engine oil technology through advanced analytics, machine learning, and artificial intelligence. In this highly visible role, you will accelerate innovation by developing predictive performance models, enabling virtual testing, and transforming complex technical data into insights that influence product development, customer solutions, and industry standards. Working at the intersection of science, data, and business strategy, you'll collaborate with global technical experts, customers, Original Equipment Manufacturers (OEMs), and cross-functional teams to solve challenging problems, advance AI-enabled product development, and drive meaningful business impact across Lubrizol's Engine Oils portfolio.
Int this role, you will:
External Engagement and Industry Influence
- Serve as Lubrizol’s senior data science representative within the automotive industry, partnering with OEMs, customers, laboratories, and industry organizations.
- Influence industry standards, testing practices, and performance methodologies through data-driven insights and advanced analytics.
- Present technical findings and publish research that advances Lubrizol’s leadership in engine oil modeling, virtual testing, and AI-enabled innovation.
- Translate complex technical and business challenges into high-impact data science initiatives that support strategic objectives.
Model Development and Deployment Leadership
- Develop, deploy, and continuously improve predictive models using statistics, machine learning, and artificial intelligence.
- Lead advanced analytics initiatives that support product development, formulation optimization, testing strategies, and industry advocacy.
- Partner with computational modeling and cheminformatics experts to enhance model performance, interpretability, and business value.
- Mentor data scientists and technical professionals while promoting best practices in modeling, analytics, and data science.
Enterprise AI Enablement
- Support the evolution of Q.LIFE® and related predictive and optimization capabilities into modern enterprise AI platforms.
- Contribute to model lifecycle management, data engineering, deployment strategies, and continuous improvement of production analytics solutions.
- Collaborate with Information Technology, Digital, Product Development, and Decision Science teams to scale AI-driven capabilities across the organization.
Business Translation and Organizational Alignment
- Bridge Decision Science, Product Development, Marketing, Technology, and Business teams to transform business challenges into actionable analytics solutions.
- Identify opportunities where advanced analytics, machine learning, and artificial intelligence can deliver measurable business value.
- Contribute to cross-functional teams to achieve strategic priorities and department objectives.
- Communicate complex technical concepts clearly to technical, business, and executive audiences, influencing decisions through data-driven insights.
Required Qualifications that Enable Your Success
- Master’s degree or Ph.D. in Statistics, Data Science, Operations Research, Computer Science, Applied Mathematics, or a related technical discipline.
- At least 8-10 years of experience applying statistics, machine learning, data science, or advanced analytics to solve complex technical and business challenges.
- Demonstrated expertise developing, validating, deploying, and improving predictive and prescriptive analytical models.
- Strong communication, stakeholder management, and influencing skills with the ability to engage scientific, technical, business, and executive audiences.
- Proven ability to work independently, solve complex problems, and mentor less-experienced team members.
- Passion for innovation and delivering data-driven solutions that generate meaningful business impact.
Preferred Qualifications that Drive You Forward
- Background in Chemistry, Materials Science, Chemical Engineering, Computer Science, or a related scientific field.
- Experience with lubricant chemistry, automotive testing protocols, industry specifications, and performance evaluation methodologies.
- Experience supporting AI-enabled product development, materials discovery, or digital innovation initiatives.
- Experience operationalizing analytical models used for business-critical decision-making.
- Knowledge of cloud-based analytics platforms, data engineering practices, and enterprise AI ecosystems.
- Experience leading digital transformation, AI, machine learning, or advanced analytics deployments within industrial environments.
- Experience developing standardized test methods, performance models, or technical evaluation frameworks.
Your Work Environment
- Standing, walking, or operating equipment for extended periods
- Working in a lab or manufacturing setting with appropriate PPE provided
- Use of computers and digital tools in an office or hybrid environment
- Occasional lifting or movement of materials
- Adherence to rigorous safety protocols and ergonomic standards
We continuously invest in our facilities and technologies to ensure they support your well-being, productivity, and growth. If you require reasonable accommodation, we are committed to working with you to ensure an inclusive and accessible experience.
Benefits that Empower You
- Competitive salary with performance-based bonus plans
- 401(k) match + Age-Weighted Defined Contribution
- Comprehensive medical, dental & vision coverage
- Health Savings Account (HSA)
- Paid holidays, vacation, and parental leave
- Flexible work environment
- Learning and development opportunities
- Career and professional growth
- Inclusive culture and vibrant community engagement
Learn more at https://benefits.lubrizol.com/
Lubrizol: Imagined for Life. Enabled by Science. ™ Delivered by You.
For nearly 100 years, The Lubrizol Corporation, a Berkshire Hathaway company, has been at the forefront of innovation to enhance everyday life, advance mobility, and make the modern world work better. Our specialty chemistry solutions—from engine oils, performance coatings, and skincare to medical devices and plumbing systems —are powered by the expertise, passion, and commitment of people like you. We tackle the world’s toughest challenges with science-based solutions, deeply understanding our customers to deliver innovative chemistry and differentiated value. Our inclusive culture, dedication to safety, and incredible global talent drive our success. Our solutions meet the evolving needs of the modern world—brought to life by science and, most importantly, delivered by you. Whether you're in the lab, on the production floor, or in the office, you'll be part of a team around the world that empowers you to think boldly, drive results, and contribute to solutions that shape a better, more sustainable future.
We win because of you. Let’s build the future together.
Job Segment: Computer Science, Research Scientist, Chemical Research, Chemistry, Chemical Engineer, Science, Technology, Engineering
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Lubrizol, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills in Demand for This Role
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Lubrizol AI Hiring
Lubrizol has 1 open AI role right now. They're hiring across Data Scientist. Based in Wickliffe, OH, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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