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About This Role
GoodRx is the leading prescription savings platform in the U.S.Trusted by more than 25 million consumers and 750,000 healthcare professionals annually, GoodRx provides access to savings and affordability options for generic and brand\-name medications at more than 70,000 pharmacies nationwide, as well as comprehensive healthcare research and information. Since 2011, GoodRx has helped consumers save nearly $75 billion on the cost of their prescriptions.
Our goal is to help Americans find convenient and affordable healthcare. We offer solutions for consumers, employers, health plans, and anyone else who shares our desire to provide affordable prescriptions to all Americans.
How We Work with AI
AI is a core part of how we operate, and as a Lead Data Scientist you are expected to help shape how AI is applied responsibly across data science and machine learning workflows.
- You evaluate emerging AI and machine learning technologies pragmatically, balancing business value, model quality, operational complexity, and responsible use.
- You identify opportunities to leverage generative AI and advanced machine learning techniques to improve modeling, experimentation, decision\-making, and team productivity while ensuring solutions remain reliable, explainable, and maintainable.
- You help establish and evolve best practices for responsible AI development, model governance, and reproducibility across the organization.
What You’ll Do
You will partner closely with data scientists, engineers, product managers, and business stakeholders to design, build, and deploy models that shape how GoodRx reaches its consumers and serves its partners. Day to day responsibilities include, but are not limited to:
- Lead complex data science initiatives across audience decisioning, marketing decision science, pharma direct measurement, and employer direct analytics, driving measurable business impact.
- Derive insights from large, complex datasets to deepen our understanding of user identity and the customer journey from online discovery to in\-store retail, connecting fragmented signals into a coherent view of how users move through the GoodRx ecosystem.
- Build audience selection, segmentation, propensity, and lookalike models that ensure the right message reaches the right user at the right moment across marketing and content channels.
- Partner with marketing and content decision science teams on content generation, channel optimization, attribution, and incrementality measurement.
- Develop measurement and modeling capabilities for pharma direct partners, including incremental prescription lift, audience targeting, and campaign effectiveness, working with first and third party prescription, claims, and behavioral data.
- Support employer direct initiatives with member engagement, utilization, and adoption modeling that strengthens our benefits and B2B offerings.
- Refine our attribution and identity capabilities, applying NLP techniques such as text cleaning, normalization, and typo correction to improve data quality at scale.
- Build predictive models using statistical and machine learning techniques across classification, regression, and disambiguation problems, owning the full lifecycle from problem framing and feature engineering through training, evaluation, deployment, monitoring, and retraining.
- Lead experimentation strategy, including A/B testing design, causal inference approaches, and measurement frameworks that inform critical business decisions.
- Define and drive the technical roadmap for decision science capabilities, introducing new methodologies, technologies, and best practices that improve team effectiveness and business outcomes.
- Provide technical leadership and mentorship to data scientists, elevating analytical best practices and advising stakeholders to ensure technical rigor and sound decision\-making.
- Partner with the broader data team to improve data consistency, cleanliness, and ease of use, contributing to shared tooling, documentation, and standards that raise the bar across the organization, including model governance, reproducibility, and responsible AI.
Must\-Have Qualifications
- 8\+ years of experience in data science, machine learning, operations research, or a related quantitative field, with a track record of delivering measurable business impact through productionized solutions. Experience in audience modeling, marketing analytics, attribution, or identity resolution is strongly preferred.
- Proven track record of technical leadership, influencing business strategy, and driving adoption of advanced analytical approaches across organizations.
- An undergraduate degree (or equivalent practical experience) in a quantitative field such as Statistics, Mathematics, Computer Science, Economics, Operations Research, Data Science, or a closely related discipline.
- Deep understanding of machine learning, statistical modeling, optimization techniques, causal inference, forecasting, experimentation, and predictive analytics.
- Expertise in Python and common data science libraries (for example pandas, NumPy, scikit\-learn, PySpark, TensorFlow, PyTorch, or similar).
- Strong working knowledge of databases and distributed data systems such as Redshift, PostgreSQL, and Spark/EMR.
- Experience building and deploying solutions using cloud platforms (AWS, GCP, or Azure) and modern data platforms such as Databricks.
- Experience deploying, monitoring, and operationalizing machine learning models using modern MLOps practices, including experimentation platforms, feature stores, and model monitoring.
- Experience evaluating and applying AI/ML solutions, including generative AI and large language model (LLM) technologies, where appropriate.
- Comfort with ambiguity and the ability to thrive in a fast\-paced, high\-change environment. You are adaptable, intellectually curious, and open to new concepts, tools, and processes.
- Strong communication skills, with the ability to influence technical and business stakeholders at multiple levels and to translate technical findings into clear, actionable recommendations for diverse audiences.
- A collaborative, self\-starting mindset. You are a team player who can operate independently, take ownership, and hit the ground running.
Nice\-to\-Have Qualifications
- Prior exposure to the prescription, pharmacy, or broader healthcare industry.
- Experience with marketing analytics, audience segmentation, attribution, or incrementality measurement.
- Experience supporting pharma manufacturer or employer and benefits partners in a B2B analytics context.
- Experience with recommendation systems, reinforcement learning, optimization engines, or decision science applications.
- An advanced degree (Master’s or PhD) in a quantitative field.
- Experience contributing to patents, publications, open\-source projects, or industry thought leadership.
What Success Looks Like
Within your first few months, you will ramp up on our data landscape and the audience, marketing, pharma direct, and employer direct problem spaces, ship your first production\-influencing analysis or model, and build trusted partnerships across the data, product, and engineering teams. Over time, you will become a go\-to expert on audience, identity, and marketing measurement modeling, and a meaningful contributor to GoodRx’s mission of making healthcare affordable for everyone.
Engineering teams are responsible for supporting appropriate security controls, including management, operational, and technical controls in addition to general GoodRx best practices, such as reading and adhering to the security policies and procedures, being vigilant and observant of potential security threats, etc.
At GoodRx, pay ranges are determined based on work locations and may vary based on where the successful candidate is hired. The pay ranges below are shown as a guideline, and the successful candidate’s starting pay will be determined based on job\-related skills, experience, qualifications, and other relevant business and organizational factors. These pay zones may be modified in the future. Please contact your recruiter for additional information.
San Francisco and Seattle Offices:
$202,000\.00 \- $323,000\.00
New York Office:
$185,000\.00 \- $296,000\.00
Santa Monica Office:
$168,000\.00 \- $269,000\.00
Other Office Locations:
$151,000\.00 \- $242,000\.00
GoodRx also offers additional compensation programs such as annual cash bonuses or commission, and annual equity grants for most positions as well as generous benefits. Our great benefits offerings include medical, dental, and vision insurance, 401(k) with a company match, an ESPP, unlimited vacation, 13 paid holidays, and 72 hours of sick leave. GoodRx also offers additional benefits like mental wellness and financial wellness programs, fertility benefits, generous parental leave, pet insurance, supplemental life insurance for you and your dependents, company\-paid short\-term and long\-term disability, and more!
We’re committed to growing and empowering a more inclusive community within our company and industry. That’s why we hire and cultivate diverse teams of the best and brightest from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has a seat at the table and the tools, resources, and opportunities to excel.
With that said, research shows that women and other underrepresented groups apply only if they meet 100% of the criteria. GoodRx is committed to leveling the playing field, and we encourage women, people of color, those in the LGBTQ\+ communities, individuals with disabilities, and Veterans to apply for positions even if they don’t necessarily check every box outlined in the job description. Please still get in touch \- we’d love to connect and see if you could be good for the role!
GoodRx is committed to providing reasonable accommodations for candidates with disabilities during our recruiting process. If you need any assistance or accommodations due to a disability, please reach out to us at [email protected].
We prioritize candidate safety. Please be aware that all official communication will only be sent from @goodrx.com or [email protected] addresses.
GoodRx is America's healthcare marketplace. The company offers the most comprehensive and accurate resource for affordable prescription medications in the U.S., gathering pricing information from thousands of pharmacies coast to coast, as well as a tele\-health marketplace for online doctor visits and lab tests. Since 2011, Americans with and without health insurance have saved $60 billion using GoodRx and million consumers visit goodrx.com each month to find discounts and information related to their healthcare. GoodRx is the \#1 most downloaded medical app on the iOS and Android app stores. For more information, visit www.goodrx.com.
Salary Context
This $185K-$323K range is above the 75th percentile 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 GoodRx, 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. This role's midpoint ($254K) sits 32% above the category median. Disclosed range: $185K to $323K.
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.
GoodRx AI Hiring
GoodRx has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $323K - $323K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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.
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