Data Scientist

We are seeking a pragmatic, end-to-end Data Scientist who can operate across the full data lifecycle, from ingestion and modeling to productionizing key data systems. This is a high-impact, high-agency role which reports directly to the CTO. Modern AI-assisted development tools make this role possible, where the data scientist can now do real engineering, too.

Responsibilities:

• Collaborate closely with other teams (Sales, Finance, Product, Marketing, and more) to translate problems and needs into action-oriented data solutions
• Design, build, and maintain data pipelines for reliable ingestion and transformation
• Rapidly prototype and iterate using AI coding tools to accelerate development and reduce toil
• Drive rigor and best practices, with a focus on data quality, consistency, and transparency
• Develop and deploy statistical models and machine learning, where appropriate
• Build clear, decision-oriented visualizations and dashboards for stakeholders across multiple departments
• Own selected production data systems: selection, orchestration, monitoring, and tuning
• Communicate and shepherd key data results and analysis to executives

Requirements:

• Experience with B2B SaaS-relevant data, including Salesforce and financial metrics
• Strong communication skills and ability to work effectively across multiple departments and stakeholder groups
• Ownership mindset and ability to deliver end-to-end outcomes independently; must be a "startup type"
• Demonstrated ability to design data pipelines and work with imperfect, evolving data sources
• Sharp attention to data quality, including validation, anomaly detection, and root-cause analysis of inconsistencies
• Strong proficiency in Python and SQL; experience with modern data stack tools (e.g., dbt, Airflow, Spark, or equivalents, a plus)
• Experience with data visualization tools (e.g., Tableau, Looker, or similar)
• Some familiarity with infrastructure and related setup (databases, data warehouses, VMs)
• Knowledge of core machine learning concepts and when to apply them pragmatically

Initial Projects:

• Build a likelihood-of-close model for Salesforce opportunities, which factors in relevant metadata and history
• Create a framework and initial implementation for an executive operational dashboard, working with a broad set of teams
• Define, validate, and implement key SaaS product-usage metrics

As we grow, you will, too, with the broad scope of a software startup.