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About This Role
About Kooner Fleet Management Solutions
Kooner Fleet Management Solutions is one of the fastest\-growing national providers of on\-site fleet maintenance, preventative service, and mobile repair solutions. With nearly a decade of industry experience, Kooner FMS helps fleets reduce downtime, extend vehicle life, and simplify operations through proactive maintenance programs and advanced technology.
As a family\-founded and field\-first company, we take pride in delivering trusted partnerships, exceptional service, and innovative fleet management solutions that keep America’s trucks and trailers road\-ready.
About the Data Scientist Role
We are looking for a Data Scientist to serve as our in\-house analytical authority, reporting directly to the CFO, embedded with the executive team, and working across every department to surface the insights that drive how we run and grow the business.
This is not a reporting role. This person builds the infrastructure and intelligence layer between our data systems and our decision\-making. They don't just find trends, they architect the pipelines, models, and systems that make those trends visible at scale. They find the money we are leaving on the table, identify where we are operating inefficiently, and deliver clear recommendations backed by rigorous statistical modeling – not just charts.
This is a hybrid opportunity based out of our corporate office in Sacramento, CA.
Where You’ll Make an Impact
- Connect and analyze data across FleetIQ, Samsara GPS, Rippling, time schedules, QuickBooks, and Power BI to build a unified view of business performance
- Identify cost\-saving opportunities and operational inefficiencies across departments; workforce, fleet, dispatch, finance, and field operations
- Develop and maintain dashboards and scorecards in Power BI and Tableau that give leadership and department heads real\-time visibility into KPIs
- Analyze technician profitability, labor cost vs. output, overtime patterns, and timecard compliance
- Evaluate GPS and fleet data against clock records to detect off\-clock vehicle use, idle time, and route inefficiency
- Assess dispatch metrics \- time to first assignment, technician response times, job completion rates \- and identify structural improvements
- Leverage AI tools and prompt engineering to accelerate analysis, automate summaries, and enhance the depth of insight from existing data
- Build and maintain master mapping tables to normalize technician names, truck assignments, territory codes, and cross\-system identifiers
- Partner with the CFO and executive team to frame business questions analytically and return with data\-backed recommendations
- Proactively surface trends, anomalies, and risks the business is not yet measuring before they become problems
What a Strong Performance Looks Like
- 60 days: You have audited all key data sources, mapped their schemas and relationships, stood up a data warehouse environment, and delivered a first cross\-department performance view to the CFO.
- 6 months: Automated data pipelines are running in production. Dashboards are self\-refreshing. You have identified and quantified at least three material cost\-saving or efficiency opportunities with model\-backed recommended actions.
- 12 months: A production ML model is influencing at least one operational process. Data is embedded in how the company makes decisions. Leaders are citing your analyses in operational and financial planning.
What Makes You a Great Fit
Required Experience
- 8–10 years minimum in data science, data engineering, or a senior analytical role with a strong engineering component
- Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or related quantitative field required; Master's degree preferred
- Background in field service, logistics, last\-mile delivery, fleet operations, or a similarly data\-rich operational business strongly preferred
- Demonstrated history of translating data findings into business decisions, not just reports
- Experience working directly with finance leadership or C\-suite as an analytical partner
- Proven ability to integrate data across multiple enterprise platforms with inconsistent formats and naming conventions
Technical Skills
- Expert\-level SQL – complex joins, window functions, CTEs, query optimization, and working with multi\-source messy data at scale
- Python at a production level – not just pandas scripts, but well\-structured, tested, version\-controlled code (OOP, virtual environments, packaging)
- Power BI and Tableau for production\-grade dashboards used by non\-technical stakeholders
- Excel at an advanced level \- pivot tables, complex formulas, model building
- Proficient with AI tools and prompt engineering to accelerate insight generation and automate routine analysis tasks
Communication \& Collaboration
- Exceptional verbal and written communication \- able to explain a complex finding in two sentences to an executive
- Comfort working cross\-functionally across operations, HR, finance, fleet, and dispatch
- Self\-directed and proactive \- you identify what needs to be measured, not just what you are asked to measure
- Comfortable presenting findings that are uncomfortable \- the data tells the truth; you communicate it clearly and constructively
Why You’ll Love Joining Our Team
- Earn What You Deserve: Competitive pay starting at $120K\-$150K based on experience.
- Weekly Paydays: Get paid every Friday – no waiting around!
- Invest in Your Future: 401(k) with company match.
- Health Benefits that Kick in Fast: Medical, Dental, and Vision coverage after just 30 Days!
- Time to Recharge: Enjoy paid vacation time, paid sick time, and paid holidays to rest, recharge, and take care of what matters most.
- We’ve Got You Covered: Life \& Disability Insurance for added peace of mind – because we take care of our team on and off the job.
- We’re Here for You: Access to our Employee Assistance Program for support when you need it most – we've got your back.
- Grow With Us: Big Career Growth Opportunities in a rapidly expanding, forward\-thinking organization.
Work Environment
- Standard office setting
- Must be able to lift up to 10 lbs
- Must be able to sit for up to 4 hours at a time
Kooner Fleet Management Solutions is an Equal Opportunity Employer. We are committed to creating an inclusive environment for all employees and applicants, free from discrimination and harassment. All qualified candidates will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law. We celebrate diversity and are dedicated to fostering a workplace where every team member can thrive.
- Kooner Fleet Management Solutions participates in E\-Verify for employment eligibility verification.
Salary Context
This $110K-$150K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Kooner Fleet Management Solutions, 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 $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($130K) sits 34% below the category median. Disclosed range: $110K to $150K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Kooner Fleet Management Solutions AI Hiring
Kooner Fleet Management Solutions has 1 open AI role right now. They're hiring across Data Scientist. Based in Sacramento, CA, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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