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About This Role
Be a part of our mission! As a world leader in creating comfortable, sustainable, and efficient climate solutions for buildings, homes and transportation, it's our responsibility to put the planet first. For us at Trane Technologies, and through our businesses including Trane® and Thermo King, sustainability is not just how we do business—it is our business. Do you dare to look at the world's challenges and see impactful possibilities? Do you want to contribute to making a better future? If the answer is yes, we invite you to consider joining us in boldly challenging what's possible for a sustainable world.
Learn about our benefits designed for you to Thrive at work and at home.
We boldly go.
Where is the work:
Virtual
What's in it for you:
Job Summary:
Remote Location - The CHVAC Analytics & Technology Solutions team mission is to harness data and technology to solve business challenges, implement AI-driven solutions, and build scalable data products to drive revenue growth, improve productivity, and add customer value.
We are seeking a senior data scientist to partner with stakeholders, program managers, and other data science functions to translate the CHVAC Analytics & Technology Solutions team’s data into value. This role collaborates across sales, IT, and other business functions to translate complex, raw data into actionable insights, leveraging advanced data science techniques to solve critical problems, drive innovation, and support strategic decision-making.
What you will do:
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Responsibilities:
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Model Development & Data Strategy: Lead the design, development, and deployment of advanced machine learning models and algorithms to solve complex business problems
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Model Deployment: Implement robust MLOps practices to ensure seamless deployment and scalability of predictive models. This includes automating model training, versioning, monitoring, and deployment processes to enable fast, reliable delivery of machine learning solutions into production environments.
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Model Evaluation: Develop robust design of experiments and statistical frameworks to evaluate and measure the impact of your data science solutions.
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Collaboration with Cross-Functional Teams: Work closely with a strong team of data engineers, product owners, sales leaders, and IT to build scalable and high-impact solutions. Collaborate on the end-to-end development process, from ideation to deployment, ensuring that data-driven solutions are seamlessly integrated into our products and services.
- Pioneer peer reviews and fosters a culture of continuous improvement and learning
- Champion best practices in data science, including data governance, model validation, and ethical AI considerations
- Highlight opportunities to use new data sources and new data science tools that are value-adding and cost effective
What you will bring:
- You have 6+ years of experience in data science & machine learning, ideally within the manufacturing, HVAC or Supply Chain industry.
- You are an expert in machine learning and statistical model development and have experience designing experiments to evaluate and measure the impact of your models.
- You have a proven track record of deploying highly impactful machine learning models into production environments
- Experience with Cloud environments, preferably GCP and AWS
- Capability to develop data science solutions in SQL, R, and/or Python, as well as utilize and adapt open-source solutions
- Proficiency to convey, in simple terms complex mathematical and statistical concepts and how they relate to the business challenge
- Strong communication skills, with the ability to work in a team and collaborate with other data professionals and business stakeholders
- Demonstrated prior experience in NLP, large language models, or generative AI
- Agile experience a plus
- Experience with ERP, CRM Systems is a plus
- Excellent written and verbal communication skills
Annual Base Salary Range or Hourly Base Pay Range:
$117,673.33 - $164,675.00
Compensation Type:
Salary
Incentive Eligible:
Yes
Sales Commission Eligible:
No
Disclaimer: We strive to provide competitive compensation for this position, tailored to a variety of factors. The actual compensation will depend on elements such as seniority, merit, geographic location, education, experience, travel requirements, and union designation. Our compensation range is generally based on the national average for the country. Additionally, benefits may vary depending on the region, business alignment, union involvement, and employee status.
Thrive at work and at home:
- Benefits kick in on DAY ONE for you and your family, including health insurance and holistic wellness programs that include generous incentives – WE DARE TO CARE!
- Family building benefits include fertility coverage and adoption/surrogacy assistance.
- Paid time off includes up to 15 vacation days, paid holidays, sick leave, and additional options to support volunteer and parental leave.
- 401K match up, educational and training opportunities through company programs along with tuition assistance and student debt support.
Disclaimer: Benefit offerings may vary by site as well as Collective Bargaining Agreements and local/state regulations
Safety Sensitive Role:
NoThe company designates certain roles as Safety Sensitive. Safety Sensitive roles may require that you pass additional drug screening.
We offer competitive compensation and comprehensive benefits and programs. We are an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, pregnancy, age, marital status, disability, status as a protected veteran, or any legally protected status.
Salary Context
This $117K-$164K range is below the median for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Trane Technologies, 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 $190,000 based on 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($141K) sits 26% below the category median. Disclosed range: $117K to $164K.
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.
Trane Technologies AI Hiring
Trane Technologies has 1 open AI role right now. They're hiring across Data Scientist. Based in Davidson, NC, US. Compensation range: $164K - $164K.
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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