Interested in this Data Scientist role at Imprint?
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About This Role
Who We Are
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Imprint helps the world's best brands grow the lifetime value of their customers. We started with co\-branded credit cards and rebuilt them to be smarter, more rewarding, and brand\-first. We partner with companies like Crate \& Barrel, Rakuten, Booking.com, H\-E\-B, Fetch, and Shell to launch modern credit programs that deepen loyalty, unlock savings, and drive growth. But the card is just the beginning. We combine advanced payments infrastructure, intelligent underwriting, and deep customer data to create delightful and personalized experiences for members as well as efficient and profitable relationships for our brand partners. Our robust technology and world\-class operations allow us and our brand partners to offer powerful financial products without becoming a bank.
In the U.S., co\-branded cards alone account for over $300 billion in annual spend, and most still run on decades\-old legacy bank systems. Imprint is the modern alternative: flexible, embeddable, and built for how people actually pay today. Backed by Kleiner Perkins, Thrive Capital, Ribbit, and Khosla Ventures, we're building a world\-class team to redefine how people pay and how brands grow. If you want to move fast, solve hard problems, and own real outcomes, we want to meet you.
The Opportunity
- Deliver analytical projects that influence product decisions, marketing campaigns, and business strategy, from problem definition through deployment and monitoring
- Build segmentation frameworks and predictive models (churn, LTV, propensity) that drive targeting, personalization, and lifecycle optimization across Imprint's partner programs
- Support A/B testing and experimentation by partnering with Product, Marketing, and Commercial teams to design, analyze, and interpret experiments using scalable frameworks and tooling
- Apply statistical inference, causal analysis, and experimentation design to improve LTV/CAC ratios and accelerate feedback loops on business performance
- Design and build agentic workflows and AI powered systems that explore data, generate hypotheses, monitor business metrics, and operationalize decisions
- Translate complex data into clear narratives for leadership, helping shape how the company thinks about growth, partner health, and customer behavior
- Contribute to team excellence through code reviews, knowledge sharing, and process improvements that raise the bar for the broader Data Science team
Your Profile
Required
- 4 to 7\+ years of experience in data science, analytics, or a related quantitative field, ideally at a high growth startup or fintech company
- Degree in a relevant field (statistics, engineering, science, finance, or similar); graduate degree is a plus
- Strong Python and SQL skills, with the ability to transform raw data, build custom datasets, and ship models to production
- Solid foundation in statistical inference, experimentation design, and causal analysis
- Active experience using LLMs and AI tools (Claude, Copilot, Cursor, or similar) as collaborators in your workflow, whether for reasoning about data, generating hypotheses, iterating on analyses, or building agentic automation
- Ability to communicate complex findings clearly to both technical and non technical audiences, including senior leadership and external partner stakeholders
- Full stack problem solving orientation: you dive into messy data, test and validate assumptions, and question everything in pursuit of the right answer
- Comfort owning projects end to end in a fast moving startup environment, collaborating cross functionally with Product, Marketing, Commercial, and Engineering to drive measurable impact
Nice to Have
- Experience in credit, lending, or card products
- Experience building or contributing to experimentation infrastructure or ML infrastructure
- Exposure to lifecycle marketing, prescreen modeling, or customer segmentation at scale
- Background in time series analysis, forecasting, optimization, or simulation
- Familiarity with dashboarding tools such as Sigma or Looker
We don't expect every candidate to check every box. If this role excites you and you bring strong fundamentals, we encourage you to apply.
Stack
Python and SQL for modeling and analysis. Snowflake for data warehousing. dbt for data transformation. Sigma for dashboarding. AWS infrastructure.
Learn More
Learn more about how we build at Imprint on our engineering blog: https://tech.imprint.co/
Perks \& Benefits
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- Competitive compensation and equity packages
- Leading configured work computers of your choice
- Flexible paid time off
- Fully covered, high\-quality healthcare, including fully covered dependent coverage
- Additional health coverage includes access to One Medical and the option to enroll in an FSA
- 20 weeks of paid parental leave for the primary caregiver and 8 weeks for all new parents
- Access to industry\-leading technology across all of our business units, stemming from our philosophy that we should invest in resources for our team that foster innovation, optimization, and productivity
*Imprint is committed to a diverse and inclusive workplace. Imprint is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. Imprint welcomes talented individuals from all backgrounds who want to build the future of payments and rewards. If you are passionate about FinTech and eager to grow, let’s move the world forward, together.*
Compensation Range: $170K \- $200K
Salary Context
This $170K-$200K 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 Imprint, 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. Disclosed range: $170K to $200K.
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.
Imprint AI Hiring
Imprint has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $200K - $200K.
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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