Senior Data Scientist

What You Will Do

Build and maintain a quality scoring classifier for support interactions using modern NLP and LLM-based approaches , including evaluation, versioning, and recurring accuracy review processes.

• Design and develop new support performance metrics and analytics that go beyond traditional volume and satisfaction reporting, such as member effort and case complexity, to better understand where the member experience breaks down and why.
• Enhance our forecasting models for support volume, capacity, and staffing demand using strategic business signals, enabling more responsive operational planning across channels.
• Drive optimal member experience interactions by building the data layer that links support interactions to unique member identities and lifecycle data, to enable analyses of support quality, NPS , and CSAT and how they relate to retention and churn.
• Partner closely with MX , operations, and cross-functional stakeholders to define the right problems, translate business needs into scalable data solutions, and bring analytical rigor to operational decision-making.
• Build recurring dashboards, reporting tools, and decision-support systems that provide clear visibility into support quality, operational performance, and member outcomes.
• Leverage strong SQL , Python , and data engineering skills to transform fragmented operational data into trusted, actionable insights and production-ready systems that drive strategic business decisions.

Requirements

We would love to have you on our team if you have:

• 6+ years of experience in data science, machine learning, or advanced analytics, with a track record of owning high-impact business problems end to end.
• Strong proficiency in SQL and Python , with experience building analytical data models, machine learning workflows, and production-quality measurement systems.
• Experience building and evaluating NLP or LLM-based models , ideally including open-source model workflows, structured evaluation, and model quality monitoring.
• Experience with forecasting, time series modeling, or other quantitative methods used to support operational planning and business decision-making over time.
• Strong data engineering intuition, including experience unifying and transforming data across multiple systems to create reliable, scalable datasets for robust downstream analysis and modeling.
• Experience working with modern cloud and data platforms such as Databricks , AWS , dbt , Snowflake , or similar tools.
• Proven ability to communicate complex analytical concepts clearly to non-technical partners and to translate model outputs into recommendations the business can act on.
• Experience partnering effectively with cross-functional and distributed teams in fast-moving environments.

Nice to Have

• Experience working with customer support, contact center, CX, or operations data.
• Experience defining or operationalizing business KPIs used to drive vendor management, service quality, or member experience improvements.
• Experience in subscription, membership, consumer technology, or digital health businesses.