Senior Data Scientist

 

Thrive Market was founded in 2014 with a mission to make healthy and sustainable living easy and affordable for everyone. As an online, membership-based market, we deliver the highest quality healthy, and sustainable products at member-only prices, while matching every paid membership with a free one for someone in need. Every day, we leverage innovative technology and member-first thinking to help our over 1,700,000+ members find better products, support better brands, and build a better world in the process. We are also a Certified B Corporation, a Public Benefit Corporation, and a Climate Neutral Certified company.

 

Join us as we bring healthy and sustainable living to millions of Americans in the years to come.

 

THE ROLE 

At Thrive Market, our ML (Data Science) team drives meaningful impact for both our members and the business by transforming data into insights, models, and systems that shape every part of the e-commerce grocery journey. From powering search, recommenders and personalization to optimizing lifecycle engagement, our data scientists ensure decision-making and product experiences are grounded in rigorous analysis and scalable intelligence.

We’re looking for a Senior Data Scientist to play a critical role within our ML team. In this role, you’ll own high-impact analytical and modeling initiatives end-to-end, partnering closely with Product, Engineering, and Analytics to influence strategy, improve customer experience, and drive measurable business outcomes. You will be expected to operate with significant autonomy, demonstrate strong judgment in problem framing and solution design, and raise the overall bar for data science excellence across the organization.

RESPONSIBILITIES: 

• Own and deliver high-impact data science initiatives, from problem definition and data exploration to modeling, validation, and productionization, driving measurable improvements in customer experience and business metrics.
• Partner closely with Product, Engineering, and Analytics to shape roadmaps, define success metrics, and translate ambiguous business problems into well-scoped analytical or modeling solutions.
• Partner with Engineering and Devops to deploy, operate, and scale ML workloads using cloud-native infrastructure and MLOps tooling (e.g., AWS SageMaker, Lambda, S3, ECR, Docker, Kubernetes).
• Design, build, and iterate on statistical models, machine learning systems, and decision frameworks (e.g., personalization, ranking, forecasting, experimentation analysis).
• Lead rigorous experimentation and measurement practices, including A/B test design, hypothesis formulation, metric selection, causal inference, and interpretation of results.
• Communicate insights, trade-offs, and recommendations clearly to both technical and non-technical stakeholders, influencing decisions at the pod and org level.
• Ensure high standards of quality and reliability in data science outputs through code reviews, model validation, documentation, and monitoring.
• Proactively identify opportunities and risks in data, modeling, or experimentation approaches, and drive improvements that enhance scalability, accuracy, and long-term impact.
• Mentor and support other data scientists through technical guidance, feedback, and best-practice sharing, without direct people management responsibility.
  
  

QUALIFICATIONS: 

• 5+ years of experience in data science, applied machine learning, or a related quantitative role, with demonstrated ownership of end-to-end projects.
• Strong grounding in ML, statistics, experimentation  and data analysis, including hypothesis testing, causal reasoning, and metric design.
• Experience building and deploying production-grade models or analytical systems in collaboration with engineering teams, including hands-on experience with cloud-based ML infrastructure (e.g., AWS SageMaker, Lambda, S3, ECR) and containerized workflows (Docker, Kubernetes).
• Proven experience designing, analyzing, and interpreting A/B tests in production environments, aligned to business or product goals, including defining success metrics and guarding against common statistical pitfalls.
• Ability to work eff