Senior Data Scientist, Analytics

Responsibilities:

• Translate complexity into action: Turn complex statistical and modeling results into clear, compelling, and actionable narratives for cross-functional partners and executive audiences.

• Uncover and scale revenue insights: Lead proactive, exploratory analyses to identify latent revenue levers, emerging trends, and root causes behind shifts in key GTM metrics—and operationalize these learnings into repeatable workflows, automated pipelines, and scalable data science operating models.

• Build revenue forecasting and performance insights: Develop and own revenue forecasting and forward-looking performance insights (e.g., pipeline health, conversion and retention drivers, scenario planning), providing a reliable “source of truth” that helps teams make faster, better commercial decisions.

• Apply advanced causal inference: Use advanced observational causal inference methods (e.g., DiD, synthetic control, DoubleML) to estimate impact and inform decisions when randomized experiments are infeasible.

• Embed AI into commercial workflows: Design and deploy AI-enabled solutions across the sales and customer lifecycle—enhancing sales calls and coaching, improving sales effectiveness, and generating proactive, transaction-based customer insights to drive retention and expansion.
  
  Who you are

  We're looking for people who meet the minimum qualifications for this role. The preferred qualifications are great to have, but are not mandatory.

  Minimum qualifications:

  • 5+ years of industry experience and an advanced degree (MS or PhD) in a quantitative field (e.g., Statistics, Computer Science, Engineering, Economics, or a related discipline).

  • Strong analytical intuition and structured problem-solving—you ask the right questions, explore data thoughtfully, and synthesize clear, defensible conclusions.

  • Excellent communicator and storyteller—able to translate technical work into crisp, actionable recommendations for both technical and non-technical stakeholders, including executives.

  • Deep curiosity about GTM performance and customer behavior—you go beyond “what happened” to understand “why it happened,” while staying pragmatic and focused on impact.

  • Strong foundations in causal inference and forecasting, with experience applying methods such as DiD, synthetic control, and modern ML-based approaches to real business problems.

  • High fluency in analytics tooling—strong SQL skills and proficiency in Python and/or R for analysis, modeling, and automation.

  Preferred qualifications:

  • Experience with Databricks or similar cloud data platforms / warehouses.

  • Familiarity with Hex or other notebook-based analysis tools.

  • Experience in a high-growth startup and/or B2B business models (e.g., pipeline, CRM, RevOps data).