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About This Role
About Leaf Home
At Leaf Home, we are powered by people on a single mission to make homeownership easy. We’ve crafted a team of the very best to ensure we make a difference by winning every day. In addition to serving our customers, Leaf Home strives to build a welcoming and inclusive workplace. We are proud to be a certified Great Place to Work® as well as a multi-year recipient of Top Workplaces recognition.
From competitive medical benefits to people-focused committees like the Women’s Committee, Diversity & Inclusion Committee, and VetConnect, we are dedicated to making Leaf Home a workplace where you can win every day. Come grow your career with us.
Why Leaf Home
- We Win – Being driven every day to win is who we are
- People Powered – Recognized as a top Ohio and U.S. workplace by Great Place to Work®, Energage, and more
- Family Feel with Enterprise-Level Resources – You are important to us, and we’ve built a culture you’ll love
- Support – Inclusion, sustainability, and reliability surround everything we do
- Personal Growth – Dedicated to providing resources and encouragement for employee growth
- Mobility – Locations throughout the U.S. and Canada, with continued expansion
Position Summary:
Leaf Home is seeking a sharp and experienced Data Scientist to join our Data Science team. In this role, you will serve as a dedicated analytical partner to Marketing while working as part of the broader enterprise Data Science organization. You will apply statistical modeling, experimentation, causal inference, and machine learning to help the organization understand marketing effectiveness, improve investment decisions, and identify opportunities for growth.
This role extends beyond developing models. You will help own and evolve analytical capabilities from problem definition and methodology development through implementation, interpretation, and business adoption. The ideal candidate combines strong statistical and technical skills with business judgment and an interest in understanding how Marketing decisions are made. While Marketing will be the role's primary area of focus, you will collaborate with Data Science peers and business partners across the organization to develop solutions that can scale across channels, verticals, and use cases.
Essential Duties and Responsibilities:
- Own and advance Marketing Science capabilities, including marketing mix modeling (MMM), incrementality measurement, attribution methodology, forecasting, and marketing optimization.
- Partner closely with Marketing leaders and channel owners to translate strategic questions into rigorous analytical approaches and clear, actionable recommendations.
- Translate complex model outputs, assumptions, uncertainty, and limitations into recommendations that Marketing and executive stakeholders can use to make investment decisions.
- Develop fit-for-purpose analytical products and tools to make Data Science outputs accessible and actionable for business users.
- Partner with experimentation resources to design and evaluate tests, connect experimental evidence with broader marketing measurement, and use causal evidence to validate and improve models such as MMM and attribution.
- Evaluate marketing effectiveness across channels, geographies, audiences, and business units, helping distinguish observed performance from incremental impact and identify opportunities to improve allocation of marketing investment.
- Contribute to the development of new Data Science capabilities in emerging areas such as workforce analytics, geographic intelligence, anomaly detection, external signal integration, operational forecasting, and AI-enabled decision support.
- Foster and deepen the culture of data-driven decision making across the organization.
- Conduct exploratory and ad hoc analyses to support evolving business needs and strategic questions.
Experience and Minimum Qualifications:
- Bachelor’s degree (or equivalent) in computer science, applied mathematics, statistics, or related discipline. Master’s degree preferred.
- Strong foundation in statistics and applied modeling, including regression, predictive modeling, model validation, uncertainty, and related quantitative methods.
- Experience with marketing measurement, marketing mix modeling, incrementality, attribution, experimentation, media optimization, or related Marketing Science applications is strongly preferred.
- Strong communication skills, including the ability to explain complex methodologies, tradeoffs, assumptions, and findings to technical and nontechnical audiences
- Must be legally authorized to work in the country of employment without sponsorship for employment visa status (e.g., H1B status).
Preferred Knowledge, Skills, Abilities or Certifications:
- Strong proficiency in Python, R, SQL, or other applicable programming languages.
Travel Requirements:
- Occasional overnight travel is required (1-10%)
Overtime/Additional Hours Requirements:
- Additional and non-standard hours may be required (exempt).
Physical Requirements:
- Normal office environment.
- Performs indoor work in a climate-controlled environment.
- Sedentary work. Exerting up to 10 pounds of force occasionally and/or negligible amount of force frequently or constantly to lift, carry, push, pull or otherwise move objects, including the human body. Sedentary work involves sitting most of the time.
Are We Your Company?
Focused on Growth Leaf Home is dominating the home improvement industry as we continue to grow. We work hard and play harder. With offices across the U.S. and Canada, our diverse team spans marketing, sales, installation, and service. We are committed to employee growth through:
- Innovating, Always – Providing cutting-edge solutions for employees and customers
- Partnering with Our Customers – Building trusted relationships at the core of everything we do
- Empowering Employees – Creating opportunities for growth and success in a supportive environment
- Supporting Our Communities – Giving back to the places our customers and employees call home
What We Offer
- Industry-leading compensation package
- Competitive medical, dental, and vision benefits after 60 days
- Retirement savings plan with company match
- Paid parental leave and generous paid time off programs
- On-campus fitness programs and meal delivery services
- Comprehensive health, wellbeing, financial wellness, and childcare benefits
- Opportunities for growth and advancement
Additional Perks
- Employee assistance program with 24/7 legal, financial, and counseling support
- Employee discount marketplace with thousands of savings options
- Flexible Gym membership
- Employee resource groups, including VetConnect and the Women’s Committee
Awards and Recognition
Leaf Home has been recognized as a top workplace by The Plain Dealer, is a multi-year recipient of Top Workplaces USA by Energage, and is Great Place to Work® Certified™. Additional recognitions include:
- Remodeling 550 list placements
- Qualified Remodeler Top 500 rankings
- Smart Culture Awards for employee-focused culture
- Inc. 5000 and Inc. 5000 Regionals: Midwest growth recognition
For a full list of awards, visit www.leafhome.com/press/.
Diversity and Equal Opportunity
Leaf Home is committed to creating a diverse and inclusive environment and is proud to be an equal opportunity employer. Employment decisions are made without regard to race, color, ancestry, national origin, gender identity or expression, sexual orientation, marital status, religion, age, genetic information, veteran status, or disability, in accordance with applicable law.
Americans with Disabilities Act Statement and Contact
Leaf Home is committed to honoring the spirit and requirements of the Americans with Disabilities Act. If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to [email protected].
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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Leaf Home, 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. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Leaf Home AI Hiring
Leaf Home has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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