Staff Data Scientist, Growth
Pomelo CareUnited States$220k – $260kPosted 26 March 2026
Job Description
About us
Pomelo Care
is the national leader in evidence-based healthcare for women and children. We deliver personalized, high-quality clinical interventions from reproductive care and pregnancy, infant care and pediatrics, to hormonal health through perimenopause and menopause, with long-term preventive care and condition management. Our model delivers 24/7 multispecialty care to address the medical, behavioral, and social factors that most significantly impact outcomes for women and children. We partner with payers, employers, and providers to expand access to quality healthcare across the system.
Role Description
Your North Star: Build innovative, data-driven products that enhance patient engagement and retention to improve the lives of moms and babies.
We're looking to bring on an experienced Staff Data Scientist to own the data strategy for our Growth and Patient Experience team. In this role, you will hold a dual focus: optimizing the top-of-funnel strategy to grow patient enrollment, and driving the development of next-generation products that keep those patients engaged throughout their care journey. Specifically, you will:
Become an expert in patient behavior, engagement patterns, and retention drivers, owning the team’s data science roadmap and playing a foundational role in shaping Pomelo's patient experience strategy
Collaborate deeply with the Marketing team to build and optimize a multi-channel enrollment strategy. You will build attribution models, analyze funnel conversion rates, and identify the best ways to allocate resources towards patient enrollment.
Define and iterate on key metrics that define the health of the business, such as enrollment and retention rates, and present insights to Pomelo’s leadership team
Complete exploratory analyses to inform development of enrollment and engagement-focused features, such as conversation topic categorization and patient journey segmentations
Build robust data pipelines and analytics infrastructure that connect external, marketing and product usage data, creating a unified view of the patient journey.
Lead the team’s approach to A/B testing across marketing and product initiatives to accurately measure impact
Collaborate closely with Product, Clinical, and Engineering teams to translate patient insights into actionable product improvements and new feature development
Who you are
You are a product-minded data scientist and innovative problem solver who thrives on building solutions that directly impact patient lives, and excited to work at a startup. Specifically,
You have 6+ years professional experience in data science, product analytics, or data engineering
You have 2+ years professional experience working in healthcare, consumer health, or high-touch consumer products
Expert proficiency in both SQL and Python, with demonstrated ability to work across the full data stack
Strong product sense with experience in product prototyping, A/B testing, and user behavior analysis
Passionate about improving maternal health outcomes and creating exceptional patient experiences
Proven track record of scoping and implementing data-driven product solutions to complex, ambiguous problems
Comfortable leading cross-functional initiatives with Product, Engineering, and Clinical teams as a strategic partner and technical expert
A strong communicator and a keen nose for value - someone who can easily and quickly understand the needs of different audiences
Experience with data visualization and dashboarding tools (e.g., Looker, Tableau, Metabase)
Experience working with modern data stack tools (e.g., dbt, Airflow, Snowflake)
Bonus points if you have any of the following
Experience with AI model development including fine-tuning, prompt engineering, model evaluation and building production AI applications
Background in healthcare AI, clinical decision support, or medical NLP
Why you should join our team
By joining Pomelo, you will get in on the ground floo ... (truncated, view full listing at source)
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