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About This Role
Fairport, NY, United States
Data Scientist – Analytics as a Service
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Position Summary
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The Senior Data Scientist is responsible for developing the advanced analytics, machine learning models and AI algorithms that power Qualitrol's Analytics as a Service portfolio. Working closely with the Product Owner, Product Engineers and Data Engineer, this individual transforms industrial data into scalable analytics services that deliver measurable customer value.
Unlike a traditional research\-oriented data science role, this position is expected to rapidly move algorithms from experimentation into production, continuously improving model performance through customer feedback, operational data and AI\-assisted development practices. Success requires balancing scientific rigor with startup execution speed.
Primary Responsibilities
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### Analytics \& Model Development
Develop advanced analytics for:
- Rotating machine condition monitoring
- Grid monitoring
- Predictive maintenance
- Fault detection
- Anomaly detection
- Asset health assessment
- Failure prediction
- Fleet benchmarking
Design algorithms that are accurate, explainable and production\-ready.
### Data Mining \& Feature Engineering
Extract insights from:
- Sensor data
- Time\-series data
- Event logs
- Operational history
- Maintenance records
- Customer operating conditions
Develop robust feature engineering pipelines to improve model accuracy and scalability.
### AI \& Machine Learning
Develop and optimize:
- Machine learning models
- Statistical models
- Generative AI applications
- Large Language Model integrations
- Predictive analytics
- Recommendation engines
Leverage AI\-assisted tools to accelerate experimentation, model development and validation.
### Production Deployment
Partner with Product Engineers to:
- Deploy models into production
- Monitor model performance
- Improve inference accuracy
- Reduce computational costs
- Continuously retrain models
Ensure analytics are scalable, reliable and maintainable.
### Customer Value Creation
Partner with Product Owner and Customer Success to understand customer use cases and translate them into differentiated analytics capabilities.
Use customer feedback and operational data to continuously improve algorithms and business outcomes.
Required Experience
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- Master's or Ph.D. in Data Science, Computer Science, Statistics, Applied Mathematics or related field
- 5\+ years developing machine learning or industrial analytics solutions
- Strong Python programming experience
- Experience with cloud\-based ML environments
- Experience deploying production AI models
- Strong statistical and analytical skills
Preferred Experience
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Experience with:
- Industrial AI
- Utilities
- Rotating machinery
- Power systems
- Time\-series analytics
- Azure Machine Learning
- AWS SageMaker
- MLOps
- LLMs and Generative AI
Success Measures
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Within 12 months:
- Multiple production analytics models deployed
- Measurable improvement in prediction accuracy
- Repeatable MLOps pipeline established
- Analytics capabilities contributing to customer adoption
- Continuous model improvement process operational
\#LI\-PW1
Ralliant Corporation Overview
Ralliant, originally part of Fortive, now stands as a bold, independent public company driving innovation at the forefront of precision technology. With a global footprint and a legacy of excellence, we empower engineers to bring next\-generation breakthroughs to life — faster, smarter, and more reliably. Our high\-performance instruments, sensors, and subsystems fuel mission\-critical advancements across industries, enabling real\-world impact where it matters most. At Ralliant we're building the future, together with those driven to push boundaries, solve complex problems, and leave a lasting mark on the world.
About Qualitrol
QUALITROL manufactures monitoring and protection devices for high value electrical assets and OEM manufacturing companies. Established in 1945, QUALITROL produces thousands of different types of products on demand and customized to meet our individual customers’ needs. We are the largest and most trusted global leader for partial discharge monitoring, asset protection equipment and information products across power generation, transmission, and distribution. At Qualitrol, we are redefining condition\-based monitoring.
We Are an Equal Opportunity Employer. Ralliant Corporation and all Ralliant Companies are proud to be equal opportunity employers. We value and encourage diversity and solicit applications from all qualified applicants without regard to race, color, national origin, religion, sex, age, marital status, disability, veteran status, sexual orientation, gender identity or expression, or other characteristics protected by law. Ralliant and all Ralliant Companies are also committed to providing reasonable accommodations for applicants with disabilities. Individuals who need a reasonable accommodation because of a disability for any part of the employment application process, please contact us at [email protected].
Pay Range
The salary range for this position (in local currency) is 104300\.00\-193700\.00
Salary Context
This $104K-$193K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Qualitrol, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($149K) sits 23% below the category median. Disclosed range: $104K to $193K.
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.
Qualitrol AI Hiring
Qualitrol has 1 open AI role right now. They're hiring across Data Scientist. Based in Fairport, NY, US. Compensation range: $193K - $193K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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