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About This Role
Why We Exist
Your car and your home are your most important assets, yet the experience of owning them is stuck in the 90s. Every part of the journey — buy/sell, insurance, maintenance, repairs — is fragmented, complicated, and expensive.
Jerry.ai is building the first app to manage it all. We started with car insurance in 2019, became one of the top 3 brokers in the U.S., then added driving insights, diagnostics, and a repair marketplace.
We've reached 5M\+ customers, raised $240M\+, scaled revenue 70X, and became profitable in early 2024\. We're now expanding into adjacent verticals (e.g. home, motorcycle, RV, etc) to become the one\-stop\-shop for all your physical assets.
Why We’re Hiring
Our Data Science \& Analytics team has been a force multiplier and accelerant to our business that has helped us reach these pivotal milestones in spite of headwinds and within a highly competitive market. They are a team of former McKinsey, BCG and Bain consultants who drive data \& insights, and inform decision\-making across every corner of our business. They are analytical powerhouses and first principles problem solvers who push forward our most important initiatives across product, growth, tech, and operations.
Every business unit has a Data Science team member embedded within it — someone who doesn't just run analyses and build dashboards, but shapes what questions we should ask, what data to pull, what conclusions to draw, what decisions to make, and then drives our teams towards our next goal: go from 5M to 50M customers and become a $10B business in the next 4 years.
As our business continues to expand, there is growing demand for more Data Science team members. We are hiring 4\-6 people to drive impact in these areas:
- Growth Marketing
- Product Development
- AI \& Automation
- Strategic Partnerships
On leveling: You may see job ads for this role at different job levels. Our priority is finding the right people, we can be flexible with job title/leveling.
Why You Will (Or Won't) Like Working Here
- No slides: If you've worked in consulting, you know what it feels like to be up at 3am reformatting a deck. That doesn't exist here. The only work that gets done at Jerry is work that will materially change the trajectory of our business — an important product, a new growth channel, a key metric, a critical partner relationship, etc.
- No corporate fluff: The Data Science \& Analytics team is 14 insatiable learners who care deeply about solving difficult problems and making a tangible impact. Every person owns a critical domain. There is no bloat, unnecessary meetings, or waiting for approvals.
- Hands\-on: This is not a pure strategy role, you will also own execution. Your day\-to\-day will be surfacing analytical insights, driving decisions, and seeing them through end\-to\-end.
What You'll Own
You'll sit on our central Data Science \& Analytics team but you’ll be embedded in one of Jerry's core business areas — growth, product, AI and automation, strategic partnerships — and own the analytical function for that business unit end\-to\-end.
- Define the right problems: Before any analysis, understand what business question it's answering and what decision it will inform. Push back if the question is flawed.
- Data and insights: Define metrics, build reports, run analyses, design experiments, and surface data insights that move your team's key metrics.
- Drive outcomes: Bring recommendations to your counterparts in product, growth, finance or operations, tell them what's happening and why, and what next steps should be.
Who You Are
- First principles thinker: You break ambiguous problems into clear hypotheses before touching data. You're analytically strong (comfortable with SQL or Python) but your instinct is to ask "what are we trying to solve here?" before jumping to "what should I build?"
- Direct communicator: You can walk a skeptical stakeholder through a complex finding and get buy\-in quickly. You write clearly, speak precisely, and don't bury the lede.
- Owner: You act like your name is on the door. If something is broken, you feel responsible for fixing it, even if it’s outside of your swim lane.
What You'll Bring
- 6\+ years of experience at a consulting firm, investment bank, or high\-growth technology company.
- Strong analytical skills; ability to pull, structure, and interpret data independently.
- Track record of owning ambiguous problems and delivering impact.
- Clear, persuasive communicator who can influence decisions at the leadership level.
Our Tools
SQL (Clickhouse), Metabase, Python, Jupyter Hub, GitHub.
*While we appreciate your interest and application, only applicants under consideration will be contacted.*
*Jerry.ai is proud to be an Equal Employment Opportunity employer. We prohibit discrimination based on race, religion, color, national origin, sex, pregnancy, reproductive health decisions or related medical conditions, sexual orientation, gender identity, gender expression, age, veteran status, disability, genetic information, or other characteristics protected by applicable local, state or federal laws.*
*Jerry.ai is committed to providing reasonable accommodations for individuals with disabilities in our job application process. If you need assistance or an accommodation due to a disability, please contact us at* *[email protected]*
*The successful candidate’s starting pay will fall within the pay range listed on this job posting, determined based on job\-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Ranges are market\-dependent and may be modified in the future. In addition to base salary, the compensation may include opportunities for equity grants.*
*We offer a comprehensive benefits package to regular employees, including health, dental, and vision coverage, paid time off, paid parental leave, 401(K) plan with employer matching, and wellness benefits, among others. Equity opportunities may also be part of your total rewards package. Part\-time, contract, or freelance roles may not be eligible for certain benefits.*
About Jerry.ai
Jerry.ai is America’s first and only super app to radically simplify car ownership. We are redefining how people manage owning a car, one of their most expensive and time\-consuming assets.
Backed by artificial intelligence and machine learning, Jerry.ai simplifies and automates owning and maintaining a car while providing personalized services for all car owners' needs. We spend every day innovating and improving our AI\-powered app to provide the best possible experience for our customers. From car insurance and financing to maintenance and safety, Jerry.ai does it all.
We are the \#1 rated and most downloaded app in our category with a 4\.7 star rating in the App Store. We have more than 5 million customers — and we’re just getting started.
Jerry.ai was founded in 2017 by serial entrepreneurs and has raised more than $240 million in financing.
Join our team and work with passionate, curious and egoless people who love solving real\-world problems. Help us build a revolutionary product that’s disrupting a massive market.
Compensation Range: $170K \- $210K
Salary Context
This $170K-$210K range is above the 75th percentile 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 Jerry.AI, 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. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $170K to $210K.
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.
Jerry.AI AI Hiring
Jerry.AI has 4 open AI roles right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $130K - $210K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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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