Senior Data Scientist, Thrive Market

• 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 effectively with cross-functional partners (Product, Engineering, Analytics, Design, Data Engineering), translating between business context and technical solutions
• Strong problem-framing and prioritization skills, particularly in ambiguous or under-specified problem spaces
• Proficiency in SQL and Python, with experience using modern data and ML tooling
• Ownership mindset: proactively identifying problems worth solving, taking accountability for outcomes, and driving initiatives forward independently

Desirable

• 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
• 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