Lead Data Scientist

GoodRx•CA HQ, CA

7d•$151,000 - $323,000•Onsite

About The Position

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.

Requirements

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 Haves

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.

Responsibilities

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.
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.