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About This Role
Job Description Who will you be working with?
The Strategic Process Excellence Data Scientist will support Quality activities by leveraging advanced analytics and Artificial Intelligence (AI) techniques to identify improvement opportunities across engineering processes and product performance.
How will you make a difference?
This role works closely with engineers, quality teams, and cross\-functional partners to analyze data, drive root cause identification, and improve control point effectiveness. The role enables data\-driven and AI\-enabled decision\-making to enhance reliability, quality, and operational performance.
What do we want to know about you?
- Bachelor’s degree in Engineering, Data Analytics, Computer Science, or related field
- Strong analytical and problem\-solving skills
- Experience with engineering or quality data
- Proficiency in Microsoft Excel and data analysis tools
- Experience with ERP systems (preferably Oracle)
- Basic knowledge of AI/ML concepts and statistical modeling
- Programming experience (Python, R, or similar preferred)
- Strong communication skills
- Ability to work with cross\-functional and global teams
- Understanding of engineering or industrial processes
Desired Characteristics:
- Experience with data visualization tools (e.g., Power BI)
- Practical experience applying AI/ML models in industrial or quality environments
- Knowledge of predictive analytics and anomaly detection techniques
- Familiarity with CAR, CoPQ, and reliability metrics
- Experience in manufacturing, supply chain, or logistics environments
- Strong attention to detail
- Ability to manage multiple priorities
- Continuous improvement mindset
- Experience integrating AI solutions into business processes
What will your typical day look like?
- Review and analyze CAR, PCR, CoPQ, and reliability data
- Perform advanced data analysis to identify trends and improvement opportunities
- Conduct root cause analysis using structured problem\-solving methods
- Analyze control point data and collaborate with cross\-functional teams
- Support analysis of field data, part usage, process metrics, and failure rates
- Interface with global supplier and engineering teams
- Develop and apply AI/ML models to detect patterns, anomalies, and predictive insights
- Automate data collection, reporting, and analysis workflows using AI\-enabled tools
- Leverage natural language processing (NLP) to analyze textual data
- Collaborate with IT and digital teams to deploy scalable analytics and AI solutions
- Communicate insights and AI\-driven recommendations to global stakeholders
- Participate in regular performance and status reviews
- Support continuous improvement of Wabtec business processes
You may also be asked to perform other duties outside of your function or trade, for which adequate training will be provided if necessary.
Wabtec will only employ those who are legally authorized to work in the U.S. for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable) and fitness for duty test (as applicable).
Additional Information
Our job titles may span more than one career level. The salary rate for this role is currently $79100\-112800 The actual salary offered to a candidate may be influenced by a variety of factors, such as: training, transferable skills, work experience, education, business needs, market demands and work location. The base pay range is subject to change and may be modified in the future. More information on offered benefits, which include health, welfare, and retirement, are available at mywabtecbenefits.com. Other benefit offerings for this role may include annual bonus, if eligible.
What could you accomplish in a place that puts People First?
At Wabtec, it’s not just about a job \- it’s about the impact you make. When our people come together, we’re Expanding the Possible by continuously improving what we do and how we do it \- for our clients and each other.
If you’re ready to revolutionize how the world moves for future generations, Wabtec is the place for you.
Who are we?
Wabtec is a leading global provider of equipment, systems, digital solutions, and value\-added services for the freight and transit rail sectors. Drawing on more than 150 years of experience, we are leading the way in safety, efficiency, reliability, innovation, and productivity. Whether it’s freight, transit, ports, logistics, mining, industrial, or marine, our expertise, technologies, and people together – are accelerating the future of transportation. With roots that date back to George Westinghouse, Thomas Edison, and Louis Faiveley, Wabtec has always built technologies and implemented solutions for a variety of sectors that are critical to meeting the needs of customers and governments alike.
Our global team of about 30,000 employees worldwide delivers performance that moves the world forward. We’re lifelong learners, obsessed with better. Learn more at www.WabtecCorp.com.
Culture powers us and the possibilities.
We believe the best ideas come from a mix of experiences and backgrounds. At Wabtec, we strive every day to create a place where everyone belongs. We’re building a culture where leadership, inclusion and your unique perspective fuel progress.
We’re proud to be an Equal Opportunity Employer. We welcome talent of all backgrounds, experiences, and identities, including race, gender, age, disability, veteran status and more.
Need accommodation? Just let us know \- we’ve got you.
Salary Context
This $79K-$112K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Wabtec, 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 Required
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 $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($95K) sits 50% below the category median. Disclosed range: $79K to $112K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Wabtec AI Hiring
Wabtec has 1 open AI role right now. They're hiring across Data Scientist. Based in Grove City, PA, US. Compensation range: $112K - $112K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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