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About This Role
THE POSITION
Our roster has an opening with your name on it
FanDuel is looking for a Data Scientist, to drive our Sportsbook customer generosity distribution and bonus spend optimization. In this role you will be responsible for customer segmentation, modeling, automation, reporting and data analysis across all areas of Sportsbook generosity distribution. Using your numerical and technology capabilities you will play a key role in bonus spend research, optimization and communication of results to CRM and Commercial senior management.
This is a great opportunity for somebody who wants to work in an environment where curiosity is rewarded, and people are empowered to make decisions. The ideal candidate will have a passion for continual learning and developing innovative solutions to business problems.
In addition to the specific responsibilities outlined above, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs.
THE GAME PLAN
Everyone on our team has a part to play
- Take on data science projects from start to finish, including working with stakeholders to agree project KPIs, and reporting on progress and outcomes throughout projects at agreed cadence
- Deliver high\-quality analysis, customer segmentation and predictive models which help drive incremental revenue through optimal automated bonus spend distribution
- Improve our understanding of customer behaviors, efficiency of promotions, and effectiveness of bonus spend
- Leverage AI to accelerate and enhance your work, taking care to ensure high integrity, managing AI risk of error, and maintaining excellent understanding of AI output to avoid blind trust.
- Through idea generation and exploratory analysis, develop new and innovative ways to turn behaviors into features for use in analysis, segmentation and predictive modeling
- Create, modify, and evolve models which solve business problems, and show demonstrable improvements to department KPIs
- Demonstrate accurate insights and predictions using easy to interpret visualizations and clear explanations of results, recommendations, methods, biases and limitations
- Work with Data Engineering and Analytics Engineer to optimize data quality and data modeling pipelines
- Take responsibility to learn the necessary skills and techniques, to develop ability to work with independence, integrity and accuracy
- Collaborate with key stakeholders to help in identifying areas for improvement, and support their strategic objectives
THE STATS
What we're looking for in our next teammate
- Minimum of 1\+ years of previous Data Science Experience
- Minimum 1\+ years building and deploying complex machine learning models with real world applications
- Have a developed technical skill set which facilitates high\-quality, efficient \& comprehensive quantitative analysis
- Proficient in SQL and experience of manipulating large datasets in Microsoft Excel
- Proficient in statistical or programming software and techniques (e.g. in Python, R, C\#, VBA, etc.)
- Experienced in explaining technical projects to non\-technical stakeholders
- Experience working with data visualization tools like Tableau an advantage
- Undergraduate degree in a numerate or technical discipline
- Interest in sports is a plus
- Interest or experience in the Sports betting industry is an advantage
ABOUT FANDUEL
FanDuel Group is the premier mobile gaming company in the United States and Canada. FanDuel Group consists of a portfolio of leading brands across mobile wagering including: America's \#1 Sportsbook, FanDuel Sportsbook; its leading iGaming platform, FanDuel Casino; the industry's unquestioned leader in horse racing and advance\-deposit wagering, FanDuel Racing; and its daily fantasy sports product.
In addition, FanDuel Group operates FanDuel TV, its broadly distributed linear cable television network and FanDuel TV\+, its leading direct\-to\-consumer OTT platform. FanDuel Group has a presence across all 50 states, Canada, and Puerto Rico.
The company is based in New York with US offices in Los Angeles, Atlanta, and Jersey City, as well as global offices in Canada and Scotland. The company's affiliates have offices worldwide, including in Ireland, Portugal, Romania, and Australia.
FanDuel Group is a subsidiary of Flutter Entertainment, the world's largest sports betting and gaming operator with a portfolio of globally recognized brands and traded on the New York Stock Exchange (NYSE: FLUT).
PLAYER BENEFITS
We treat our team right
We offer amazing benefits above and beyond the basics. We have an array of health plans to choose from (some as low as $0 per paycheck) that include programs for fertility and family planning, mental health support, and fitness benefits. We offer generous paid time off (PTO \& sick leave), annual bonus and long\-term incentive opportunities (based on performance), 401k with up to a 5% match, commuter benefits, pet insurance, and more \- check out all our benefits here: FanDuel Total Rewards. \*Benefits differ across location, role, and level.
FanDuel is an equal opportunities employer and we believe, as one of our principles states, "We are One Team!". As such, we are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or any other characteristic protected by state, local or federal law. We believe FanDuel is strongest and best able to compete if all employees feel valued, respected, and included.
FanDuel is committed to providing reasonable accommodations for qualified individuals with disabilities. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please email [email protected].
*The applicable salary range for this position is $98,000 \- $128,100* *USD, which is dependent on a variety of factors including relevant experience, location, business needs and market demand. This role may offer the following benefits: medical, vision, and dental insurance; life insurance; disability insurance; a 401(k) matching program; among other employee benefits. This role may also be eligible for short\-term or long\-term incentive compensation, including, but not limited to, cash bonuses and stock program participation. This role includes paid personal time off and 14 paid company holidays. FanDuel offers paid sick time in accordance with all applicable state and federal laws.*
*It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.*
\#LI\-Hybrid
Salary Context
This $98K-$128K range is in the lower quartile 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 FanDuel, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($113K) sits 41% below the category median. Disclosed range: $98K to $128K.
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.
FanDuel AI Hiring
FanDuel has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $128K - $128K.
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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