Data Scientist, Support

About the Role

The successful candidate will have a strong background in analytics, business intelligence, or data science, with experience working with customer support or operations teams. You will be responsible for designing, building, and maintaining self-serve dashboards and reporting tools, leveraging SQL, Python, and LLMs to drive insights and recommendations.

The User Operations team works closely with Sales, Technical Success, Product, Engineering, and other stakeholders to deliver the best possible experience to customers. As a Data Scientist, Support, you will be an integral part of this team, collaborating with cross-functional partners to drive decision-grade insights and inform strategic recommendations.

What You Will Do

• Explore large support and product datasets to uncover trends, volumes, and user-experience pain points
• Build, enhance, and maintain self-serve dashboards and reporting tools, enabling non-technical teams to answer their own data questions
• Establish a unified metrics taxonomy for service-health and performance
• Leverage LLMs to build bespoke classifiers that automatically label and segment inbound volumes
• Partner with Data Engineering to ensure reliable pipelines, implement data-quality checks, and document sources of truth
• Collaborate with Data Science on predictive models and experimentation, translating results into operational recommendations
• Prototype quickly, leveraging ChatGPT, Jupyter notebooks, Retool, and other tools to prove value before hardening with Engineering
• Jump into high-priority special projects to conduct bespoke deep-dive analyses and deliver clear, strategic recommendations to leadership

What We Are Looking For

• 8+ years of experience in analytics, business intelligence, or data science
• Expert-level SQL skills and proficiency in Python or R for advanced analysis and automation
• Hands-on experience designing and maintaining BI dashboards with a focus on clarity and self-serve usability
• Hands-on experience fine-tuning or prompt-engineering LLMs to build text classifiers, sentiment analysis, or tagging systems
• Demonstrated ability to translate complex datasets into clear business stories and recommendations for both technical and non-technical audiences
• Familiarity with support metrics (SLAs, FCR, deflection) and ability to define service health KPIs
• Strong cross-functional communication skills, comfortable collaborating daily with engineers, data scientists, and operations leaders

Nice to Have

• Experience working with customer support or operations teams
• Knowledge of support metrics and ability to define service health KPIs
• Familiarity with data engineering principles and practices

Benefits and Perks

• Competitive salary and equity package
• Opportunity to work with a cutting-edge AI company
• Collaborative, dynamic work environment
• Flexible remote work arrangements, with 3 days in the office per week
• Professional development opportunities, including training and conference sponsorship
• Access to the latest tools and technologies, including LLMs and data science platforms
• Comprehensive health insurance and benefits package
• Generous paid time off and vacation policy
• Relocation assistance for new employees

• Develop a strong portfolio of data science projects, showcasing your ability to analyze complex datasets and communicate insights effectively.
• Familiarize yourself with LLMs and their applications in data science, including text classification, sentiment analysis, and tagging systems.
• Practice prototyping quickly, using tools like ChatGPT, Jupyter notebooks, and Retool to prove value before hardening with Engineering.
• Be prepared to discuss your experience working with cross-functional teams, including engineers, data scientists, and operations leaders.
• Highlight your ability to define service health KPIs and familiarity with support metrics, including SLAs, FCR, and deflection.
• Showcase your expertise in SQL, Python, or R, and your experience designing and maintaining BI dashboards.
• Prepare to discuss your experience working with large datasets, including data preprocessing, analysis, and visualization.