Snowflake

GTM Staff Data Scientist

Menlo Park, California, United States · Work style not stated · FullTime

USD 184,000–264,500/year

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About the role

Description from the employer’s posting. Check the employer’s page for the current version.

  • EmployerSnowflake
  • LocationMenlo Park, California, United States
  • Work styleWork style not stated
  • Employment typeFullTime
  • Employer posted
  • Salary (as stated by the employer)USD 184,000 – 264,500 per year

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.

Our Data Analytics and AI org (DAA) is actively seeking a Staff Data Scientist, GTM to provide technical leadership for Snowflake’s next generation of AI & Machine Learning powered GTM decision systems.

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You will contribute to high-impact work across sales and marketing: propensity models across the GTM funnel, measuring the causal effect of GTM investments and interventions, and recommending actions for accounts, leads, opportunities, and customers.

This role goes beyond developing models. You will define how decision systems are designed, evaluated, productionized, and integrated into the workflows of sellers, marketers, and business leaders. You will establish reusable technical standards, guide investment across use cases, and ensure that sophisticated methods translate into measurable business impact.

What You’ll Do

• Set the technical direction for a portfolio of AI & Machine Learning GTM decision systems spanning Sales and Marketing.

• Develop pipeline forecasting methods that model stage progression, conversion, deal timing

• Build account, lead, opportunity, and customer models that identify propensity, risk, potential, and likely next outcomes.

• Develop recommendation and next-best-action systems that determine where GTM teams should focus, which action to take, and when to take it.

• Apply causal inference, experimentation, and uplift modeling to measure the incremental impact of campaigns, sales activities, and customer interventions.

• Define common standards for point-in-time training, backtesting, calibration, ranking quality, treatment-effect evaluation, uncertainty, and realized business impact.

• Partner with GTM leaders and RevOps to identify high-value decisions, define interventions, and embed outputs into recurring workflows.

• Separate genuine customer and market movement from CRM changes, selection effects, territory shifts, instrumentation gaps, and model artifacts.

• Mentor scientists and raise technical standards across GTM Data Science and its partner teams.

What We’re Looking For

• Advanced degree in Statistics, Mathematics, Operations Research, Economics, Engineering, Computer Science, or a related quantitative field, or equivalent practical experience.

• 5+ years of experience building production-grade statistical or machine learning systems with meaningful business impact.

• A record of setting technical direction across ambiguous, cross-functional, or multi-team problem spaces.

• Deep expertise in several relevant areas, such as causal inference, experimentation, forecasting, propensity modeling, uplift modeling, ranking, or recommendation systems.

• Strong judgment about when to use predictive ML, causal methods, generative AI, or a simpler analytical approach.

• Experience translating business decisions into measurable objectives, interventions, evaluation designs, and production systems.

• Strong Python and SQL skills and experience working with large-scale data platforms.

• Experience operating models with monitoring, validation, versioning, reproducibility, and safe lifecycle management.

• Ability to work with imperfect CRM, marketing, product, and customer data while making assumptions and limitations explicit.

• Demonstrated ownership of high-stakes outputs used by business or executive stakeholders.

• Excellent communication, technical leadership, and cross-functional influence skills.

Especially Valuable Experience

• B2B SaaS, enterprise sales, consumption-based businesses, or account-based GTM motions.

• Pipeline forecasting, account prioritization, lead or opportunity scoring, expansion, renewal, or churn modeling.

• Incrementality testing, causal measurement, uplift modeling, or marketing effectiveness.

• Recommendation systems, next-best-action models, ranking, or decision optimization.

• LLMs, agents, retrieval systems, or AI-assisted Sales and Marketing workflows.

• CRM, marketing automation, product telemetry, customer success, and unstructured interaction data.

• Deploying model outputs into business workflows and measuring adoption and realized impact.

Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.

How do you want to make your impact?

For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com

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ROLIVA currently lists 354 open roles like this in United States.

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  • USD a year, after annualising126,000–423,000 (40 listings, 15 employers)

At a glance

  • Employment typeFullTime
  • SeniorityLead / Principal
  • Salary (as stated by the employer)USD 184,000–264,500 per year
  • Employer posted
  • Added to ROLIVA
  • Last checked

Skills in this posting

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SQL Python Snowflake Machine learning

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