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About This Role
Location
Hybrid - Denver
Employment Type
Full time
Location Type
Hybrid
Department
Technical - Non-Engineering
Ibotta is seeking a Data Scientist, Marketing Analytics to join our Publisher Insights team and contribute to our mission to Make Every Purchase Rewarding. Our team leverages the latest developments and best practices in data analyses, testing methodologies, and statistics to provide actionable insights to the Marketing Team.
As a Data Scientist, you’ll work on our Rewards Optimization models, which use uplift modeling to deliver personalized treatments in our weekly retention programs. You’ll own these models end to end, from working with stakeholders and preparing data through model development, production deployment, and ongoing monitoring. You’ll be able to advance not only your technical skills, but your organizational and leadership skills as well, by contributing to the creation of best practices for the team and company, and learning from the more senior scientists and managers on the team.
This position is located in Denver, Colorado as a hybrid position requiring 3 days in office (Tuesday, Wednesday, and Thursday). Candidates must live in the United States.
Not based in Denver? We will offer a relocation bonus to help make your move to the Mile High City a smooth one.
What you will be doing:
- Build, retrain, and tune the uplift models that decide which bonus each saver receives in our weekly retention programs, balancing incremental redemptions against cost.
- Own these models in production: maintain feature pipelines, deploy new model versions, and monitor performance and drift over time, following Ibotta’s model development standards.
- Manage weekly model operations: review results as they come in, adjust model parameters to stay on budget, and within performance thresholds.
- Help design and analyze the A/B tests and holdouts that measure bonus performance and produce training data for our models.
- Report model performance and business impact to Marketing stakeholders, and explain results clearly to non-technical audiences.
- Analyze large datasets on saver behavior to find insights that improve our models and marketing programs.
- Work with analytics engineers, data engineers, and Marketing partners to find, define, and prepare the data needed for analysis and modeling.
- Contribute to team best practices, including documentation, code review, and reusable modeling workflows.
- Embrace and uphold Ibotta’s Core Values: Integrity, Boldness, Ownership, Teamwork, Transparency, & A good idea can come from anywhere.
What we are looking for:
- 2+ years of progressive experience in a professional data science, machine learning, statistics, or data analysis role, or related experience.
- Bachelor’s degree in Computer Science, Mathematics, Statistics, Data Science, Economics or similar field required; Advanced degree preferred.
- Deep Python (pandas, scikit-learn) and SQL skills. Spark or PySpark experience is a strong plus.
- Hands-on experience building and evaluating machine learning and statistical models, such as gradient boosting and neural networks (TensorFlow/Keras or PyTorch).
- Working knowledge of experimentation and causal inference. Exposure to uplift or heterogeneous treatment effect modeling is a strong plus.
- Experience deploying or maintaining at least one model in production, using Git and an orchestration tool such as Airflow or Databricks Jobs. Experience with model monitoring and drift detection is a plus.
- Experience building features from large, event-level datasets.
- Comfortable using AI coding assistants, and able to review, test, and debug the code they produce.
- A passion for driving important decisions using data and data storytelling, including explaining model results and trade-offs to non-technical partners.
- Experience with marketing incentives, promotions, loyalty, or retention programs, or with models that balance competing goals such as volume and cost, is a plus.
About Ibotta (“I bought a…”)
Ibotta (NYSE: IBTA) is a leading performance marketing platform allowing brands to deliver digital promotions to over 200 million consumers through a network of publishers called the Ibotta Performance Network (IPN). The IPN allows marketers to influence what people buy, and where and how often they shop – all while paying only when their campaigns directly result in a sale. American shoppers have earned over $2.6 billion through the IPN since 2012. The largest tech IPO in history to come out of Colorado, Ibotta is headquartered in Denver, and is continually listed as a top place to work by The Denver Post and Inc. Magazine.
To learn more about what our Tech teams are doing day to day, visit Building Ibotta on Medium.com.
Additional Details:
- This position is located in Denver, CO and includes competitive pay, flexible time off, benefits package (including medical, dental, vision), Employee Stock Purchase Program, and 401k match. Denver office perks include paid parking, snacks, and occasional meals.
- Base compensation range: $110,000 - $130,000. Total compensation for this role also includes a variable component in addition to base salary. Equity is included in overall compensation package. This compensation range is specific to the United States labor market and may be adjusted based on actual experience.
- Ibotta is an Equal Opportunity Employer. Ibotta’s employment decisions are made without regard of race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected status.
- Applicants must be currently authorized to work in the United States on a full-time basis.
- Applicants are accepted until the position is filled.
- For the security of our employees and the business, all employees are responsible for the secure handling of data in accordance with our security policies, identifying and reporting phishing attempts, as well as reporting security incidents to the proper Channels.
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Recruiting Agency Notice
Ibotta does not accept agency resumes and is not responsible for any fees related to unsolicited resumes. Please do not forward resumes to any Ibotta employees.
Salary Context
This $110K-$130K range is in the lower quartile for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Ibotta, 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. This role's midpoint ($120K) sits 37% below the category median. Disclosed range: $110K to $130K.
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.
Ibotta AI Hiring
Ibotta has 1 open AI role right now. They're hiring across Data Scientist. Based in Denver, CO, US. Compensation range: $130K - $130K.
Location Context
AI roles in Denver pay a median of $197,900 across 126 tracked positions. That's 7% below the national median.
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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