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About This Role
*MaintainX is the world's leading AI\-powered maintenance and asset management platform, serving 13,000\+ customers including Duracell, Shell, Cintas, and Brenntag. We raised* *$150M in Series D funding* *led by Bessemer Venture Partners and Bain Capital Ventures, bringing our total funding to $254M. We were named to the* *Forbes 2025 Cloud 100**, the definitive ranking of the top 100 private cloud companies in the world. We're growing fast and hiring the talent to match.*
### About MaintainX
MaintainX is the world's leading mobile\-first work execution platform for industrial and frontline teams. We help over 13,000 customers — including Duracell, McDonald's, Shell, DHL, and Volvo — reduce unplanned downtime and run more efficient operations.
In July 2025 we closed a $150M Series D led by Bessemer Venture Partners, bringing our total funding to $254M at a $2\.5B valuation. We were named to the 2025 Forbes Cloud 100 and ranked \#1 in EAM and CMMS on G2's Summer 2025 report.
The market we serve is changing fast: 68% of industrial companies have the same or more downtime than they did in 2024, and there are 3\.8M unfilled manufacturing jobs projected through 2033\. Our work matters because real plants, real machines, and real frontline workers depend on it.
### The Role
We're hiring a Senior Product Data Scientist to join our Product Analytics team. You'll be the first dedicated Data Scientist on a team that has operated as a product analytics function — meaning you'll define what rigorous experimentation and ML\-driven insight looks like at MaintainX, and directly influence how our product evolves.
- Design and lead product experimentation programs (A/B tests, causal inference) that drive measurable improvements in product adoption and retention
- Build and deploy ML models — GBMs, random forests, logistic regression — that turn product signals into actionable decisions for PMs and engineering leads
- Partner cross\-functionally with Product, Design, and Engineering to frame business problems as data problems and translate findings into decisions
- Translate model outputs and experimental results into clear business narratives for senior stakeholders and executives
- Establish best practices for experimentation and ML workflows
### You Have
Must\-haves:
- Senior Product Data Scientist or Senior Data Scientist title with 5–7 years of relevant experience
- Hands\-on experience with product experimentation — A/B testing, causal inference, statistical significance
- Python proficiency including scikit\-learn and MLflow
- Experience building ML models (GBMs, random forest, linear/logistic regression)
- Background in B2B SaaS or B2C SaaS
- Strong cross\-functional communication — you've worked directly with PMs, engineers, and designers, not just a sales or finance audience
- Executive presence: you can turn a complex model output into a business recommendation a VP can act on
Nice\-to\-haves:
- Experience with Databricks
- Familiarity with Amplitude or similar event analytics platforms
### We Offer
- Competitive base \+ equity in a high\-growth, post\-Series D company
- Day\-1 benefits coverage — health, dental, vision
- Unlimited PTO
- $500 home office stipend · $1K annual L\&D budget
- Hybrid hubs in Montreal, Toronto, Raleigh, Miami, and Bay Area
### How We Work
We reward output. We promote quickly when people deliver, and we have honest conversations early when fit isn't right. If you're energized by ambiguity, care about the people our platform serves, and want to do the most impactful data science work of your career — we'd love to talk.
*Our mission is to deliver one platform for maintenance, repair \& operations teams to keep the physical world running. We believe the greatest asset in any organization is the people. That’s why we built an intuitive, mobile\-first solution to help boost productivity and collaboration across teams and locations.*
*MaintainX is committed to creating a diverse environment. All qualified applicants will receive consideration for employment without regard to race, colour, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.*
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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At MaintainX, 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 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
MaintainX AI Hiring
MaintainX has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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