Interested in this Data Scientist role at Lemonade Insurance Company?
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About This Role
New York, United States \| Tech Development \| Full\-time
Not your everyday company
Lemonade completely reinvented how insurance works. As a customer\-centric tech company, we created an insurance experience that is smart, instant, and delightful.
At Lemonade, you’ll be working with a group of like\-minded makers, who get a kick out of moving fast and delivering great products. We surround ourselves with some of the smartest, most motivated, creative people who are filled with positive energy and good karma.
Unlike most publicly traded companies, we’re nimble and efficient. We take pride in the fact that we still think and operate like a startup. We don’t care much about titles and hierarchy and instead focus on innovation, bold moves, and challenging the status quo.
We’re built as a lean, data\-driven organization that relies on a common understanding of objectives and goals to provide teams with autonomy and ownership. We don’t like spending our days in meetings and we skip committees altogether. At Lemonade, there’s no such thing as going over someone’s head. We have zero tolerance for bureaucracy, office politics, and lean\-back personalities.
As a Public Benefit Corporation and a certified B\-Corp, we deliver environmental and social impact using our products and tech. Through our Giveback program, we partner with organizations such as the ACLU, New Story, The Humane Society, Malala Fund, American Red Cross, 360\.org, charity: water, and dozens of others, and have donated millions towards reforestation, education, animal rights, LGBTQ\+ causes, access to water, and more.
Awarded ‘best workplace’
Best Workplace and Best\-Led Company by Inc. Magazine
“World Changing” by Fast Company
Recognized as a World Changing Idea by Fast Company Magazine
Ranked \#1 Home insurance in America
Won best renters and homeowners insurance in America by US News, and others
Best Pet Insurance in America
Rated “Best Overall Pet Insurance” by Better Homes \& Gardens
Rated 4\.9 on the App Store
Among the highest rated apps of all time
What you’re applying for
We're looking for a Senior AI Data Scientist to help push Lemonade's pricing models into genuinely new territory. You'll sit within our Data Science Pricing team, working closely with Actuarial and Insurance Product to develop and deploy predictive models across our Home, Renters, Car, and Pet lines — in multiple countries. This isn't a research\-only role. You'll own projects end\-to\-end, from scoping the business problem through production deployment, and you'll help set the technical bar for the data scientists around you.
We believe three things matter for every role at Lemonade: *drive* to push through challenges, *efficiency* that keeps standards high while moving fast, and *adaptability* that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work.
Our AI\-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role you'll
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- Research and apply advanced ML and AI solutions to real pricing and underwriting problems — not just prototype them, but get them into production
- Own projects end\-to\-end, from high\-level business strategy through deployment, driving alignment across technical and non\-technical stakeholders
- Collaborate with the Insurance team to develop, file, implement, and monitor new predictive models across Home, Renters, Car, and Pet products
- Mentor junior data scientists and establish technical standards that raise the bar across the team
- Champion the adoption of advanced data science practices throughout the broader Insurance organization
What you'll need
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- 2\-4\+ years of practical data science experience, with a track record of shipping production\-grade work at scale
- Fluency in Python for both software development and model building, plus strong SQL skills working with large datasets
- Demonstrated ability to scope and structure technical solutions from ambiguous business problems — independently, without waiting for the problem to be handed to you pre\-packaged
- Proven impact beyond your own deliverables — infrastructure, tooling, or standards others actually adopted
- Deep familiarity with the current AI and ML landscape, and a genuine drive to push its application further
- Experience in InsurTech or FinTech is a plus
- Master’s or Ph.D. in Data Science, Statistics, Computer Science, or a related quantitative field preferred—or a Bachelor’s degree/equivalent practical experience with a strong track record of applied data science
- Ability to work in our Soho, NYC office 3 days per week
*Please note that we are unable to sponsor applicants for work visas.*
*Unfortunately, we cannot consider applicants from these states: Alaska, California, Colorado, Montana, Hawaii, New Mexico and Puerto Rico.*
Lemonade's US base salary range for this full\-time position is $162,000 \- $175,500 plus equity \+ benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job\-related skills, experience, and relevant education or training. Our competitive total rewards package includes comprehensive health \& wellness coverage, equity, a 401(k) plan with employer match, 20 paid vacation days, and parental leave. Speak to your recruiter to hear more about the specific salary range for your preferred location and our total rewards package.
Things to know…
Lemonade is an equal opportunity employer committed to diversity and inclusivity. We never discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. If you require reasonable accommodations due to religious beliefs, pregnancy, or disabilities, let us know at any time.
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Salary Context
This $162K-$175K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Lemonade Insurance Company, 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 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($168K) sits 13% below the category median. Disclosed range: $162K to $175K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Lemonade Insurance Company AI Hiring
Lemonade Insurance Company has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $175K - $175K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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