Data Scientist (Analytics)

THE ROLE

We’re hiring a Data Scientist to help build and scale our internal analytics capabilities. This is a foundational role where you’ll create dashboards, models and insights to power business and product teams alike. You’ll collect requirements, define key metrics, and deliver recommendations directly to stakeholders. You'll define what success looks like across a technical, usage-based platform and turn ambiguous questions into analyses, forecasts, and experiments that shape Baseten’s product and strategy.

RESPONSIBILITIES

• Build and maintain production-grade models and dashboards across multiple functions with a focus on accuracy, simplicity and user experience.
• Define and instrument core metrics around capacity, availability, ROI, product adoption, revenue and costs.
• Ingest and transform raw data using tools like dbt, Airbyte, and Databricks.
• Partner with Engineering, Finance, Marketing, and Sales teams to understand goals and translate them into data solutions
• Analyze product, operational and commercial data to identify opportunities, quantify tradeoffs, and influence product and GTM priorities.
• Present findings and recommendations to technical and nontechnical audiences, including company leaders.

REQUIREMENTS

• 5+ years of experience in analytics, BI, or data engineering roles
• Strong SQL skills with the ability to write complex queries for data transformation and analysis
• Experience designing medallion data architectures, including raw, conformed, and business-ready models with testing, documentation, and lineage.
• Excellent communication skills and a collaborative approach to working with cross-functional teams
• Demonstrated ability to explain complex work and make clear recommendations to technical and business audiences.

NICE TO HAVE

• Experience with AI infrastructure, developer platforms, or inference.
• Hands-on forecasting expertise, including ARIMA, Prophet, or comparable time-series methods, with disciplined backtesting, error analysis, and scenario planning.
• Working knowledge of model-serving and training workflows and the infrastructure metrics that shape performance and cost.
• Practical experimentation experience, including test design, power analysis and knowing when directional evidence is sufficient to act.