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About This Role
Progressive Leasing is a leading provider of in-store and e-commerce lease-to-own solutions. With more than 20 years in FinTech, we’ve grown from start-up to industry leader by innovating, simplifying, and valuing people. We are a subsidiary of PROG Holdings (NYSE: PRG), a FinTech holding company with three business segments: Progressive Leasing, Purchasing Power (a leading employee purchase program for consumer products and services using payroll deduction), and Four, a Buy Now Pay Later (BNPL) platform.
Progressive Leasing is a leading provider of in-store and e-commerce lease-to-own solutions. As an almost 20+ year old FinTech company that has gone from start-up to industry leader, we know how to innovate, simplify, and value all people. We are a company founded on our grit and we are constantly looking to the future. As an ever-evolving group of entrepreneurs and technologists, we strive to do the right thing period in all aspects of our work.
We are a subsidiary of PROG Holdings (NYSE: PRG), an exciting FinTech holding company, with three business segments including Progressive, Purchasing Power, and Four, a Buy Now Pay Later (BNPL) platform.
We are currently hiring a Senior Data Scientist to help grow our company and ensure our mission is achieved! In this role, you will lead the development of the decision systems and predictive models that drive our business, turning rigorous analysis into measurable impact.
This is a work-from-home position that can be performed remotely anywhere in the continental US or from our corporate office in Utah.
Employee Value Proposition (EVP): PROG is dedicated to providing people with opportunity; opportunity for inclusive collaboration, opportunity for innovation, and opportunity for development.
WE ARE: We're an entrepreneurial, high-ownership team building data-driven products in the Fintech space. We set a high bar for ourselves and we expect everyone on the team to do the same. We move fast, but we're deliberate: decisions are grounded in data, rigorous analysis, and a deep respect for real-world impact.
YOU ARE: Driven analytics professional. As a Senior Data Scientist, you will blend analytics, data mining, machine learning, and software engineering to build solutions that will result in massive improvements to the way we do business. You will lead the development and improvement of key decision systems, predictive models, and business processes to drive maximum business impact. You will develop a profound understanding of our customers, products, and financials by studying the data that describe each of these elements. You'll be at the forefront of this rapidly evolving field and have the opportunity to develop your own tools and technologies, while helping set technical direction for the team. Most importantly, you'll be part of a team that forms the backbone of a company at the forefront of an exciting, growing industry.
YOUR DAY-TO-DAY:
- Lead the design, build, and maintenance of decision systems and predictive models that are key to Progressive Leasing's competitive advantage
- Explore new data sources, both internal and external to Progressive, and use findings to further improve model performance
- Design, implement, and read in-market experiments testing new algorithms, technologies, and processes
- Prototype solutions to ambiguous business problems using cutting-edge technologies
- Recommend and drive strategic changes through rigorous analytics and creative problem solving, and communicate findings to senior stakeholders
- Identify, evaluate, and lead the implementation of advanced analytics methods
- Mentor and support junior data scientists, and contribute to code reviews, modeling standards, and best practices
- Be part of a collegial team of like-minded data scientists!
YOU'LL BRING:
- 4+ years of experience
- Master's degree in a quantitative / technical field (Math, Stats, Econ, Eng, Physics, CompSci)
- Deep experience with R, Python, machine learning tools, and SQL
- Proven track record of applying advanced models to real-world problems and delivering measurable business impact
- Ability to craft rigorous research / evaluation designs
- Ability to independently own projects end to end, from problem definition through deployment
- Strong communication skills, including explaining technical results to non-technical audiences
- Bonus: Experience in engineering environments
- Bonus: Knowledge of DevOps and Git
- Bonus: A track record of agentic AI use and implementation
- Bonus: Experience mentoring or leading technical projects
- AI-enabled tools are already part of how work gets done across our organization, and their use will continue to expand over time. We value people who are curious, adaptable, and open to learning as roles and workflows evolve
WE OFFER:
- Competitive Compensation
- Full Health Benefits; Medical/Dental/Vision/Life Insurance + Paid Parental Leave
- Company Matched 401k
- Paid Time Off + Paid Holidays + Paid Volunteer Time
- Diversity Alliance Resource Groups
- Employee Stock Purchase Program
- Tuition Reimbursement
- Charitable Gift Matching
- Job Required Equipment & Services Will Be Provided
Progressive Leasing welcomes and encourages diversity in the workplace. We do not discriminate in any aspect of employment on the basis of race, color, religion, national origin, ancestry, gender, sexual orientation, gender identity and/or expression, age, veteran status, disability, or any other characteristic protected by federal, state, or local employment discrimination laws where Progressive Leasing does business.
Progressive Leasing welcomes and encourages diversity in the workplace. We do not discriminate in any aspect of employment on the basis of race, color, religion, national origin, ancestry, gender, sexual orientation, gender identity and/or expression, age, veteran status, disability, or any other characteristic protected by federal, state, or local employment discrimination laws where Progressive Leasing does business.
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 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Prog Leasing, LLC, 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 $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Prog Leasing, LLC AI Hiring
Prog Leasing, LLC has 1 open AI role right now. They're hiring across Data Scientist. Based in UT, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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