Senior Data Scientist

Salary Range: 148500 to 170500 (Currency: USD) (Pay period: per-year-salary)

ABOUT THE TEAM

The Growth team connects market insight, data quality, and operational execution to help Immuta make better business decisions. We work across the organization to improve how the company understands its market, prioritizes opportunities, measures performance, and turns data into action.

This role sits at the intersection of data science, analytics engineering, and business strategy. Reporting to the Director of Growth Operations, you’ll join a lean, hands-on team where success depends on combining strong technical judgment with practical business understanding. Your work will help establish trusted data workflows, evaluate the signals that matter, and improve how Immuta makes decisions about accounts, opportunities, and revenue programs.

ABOUT THE ROLE

As a Senior Data Scientist, you’ll own the path from raw data to decisions that leaders can act on. You’ll bring new data into Snowflake, model it in dbt to production standards, investigate open questions about Immuta’s market and revenue motion, and build the evidence behind better decisions.

You’ll work on questions such as which intent and engagement signals meaningfully precede opportunity creation, whether account-readiness scores predict conversion, how partner overlap relates to deal outcomes, and how to evaluate event performance while accounting for pre-existing buyer intent.

This is a highly hands-on, individual contributor role for someone who enjoys working across the full data lifecycle. You’ll be expected to define the question, choose an appropriate analytical approach, communicate uncertainty clearly, and help stakeholders move from analysis to action.

CORE RESPONSIBILITIES

Own the reliability of the revenue pipeline data used for business reporting and decision-making. Investigate data issues, identify root causes, resolve problems, and document definitions and validation practices.
Bring new data sources into Snowflake, including enrichment, intent, partner, and product-usage data, using Snowpipe, APIs, and related tools, and build and maintain production-quality dbt models that make data reliable, reusable, and accessible to business and technical stakeholders.
Translate ambiguous business questions into testable hypotheses with clearly defined outcomes and success criteria.
Build, validate, and improve scoring and prioritization models, including account-readiness, ideal customer profile, and lead-qualification models.
Apply regression, hypothesis testing, experimental and quasi-experimental design, and uncertainty quantification to questions involving small, incomplete, or messy business datasets.
Communicate findings, recommendations, assumptions, and limitations to business leaders and other stakeholders in a way that supports clear decision-making.
Automate recurring analysis and reporting so that repeatable questions can be answered through reliable workflows rather than manual effort.
Improve documentation, analytical standards, and data practices as the Growth and Revenue Operations function scales.

REQUIRED EXPERIENCE

Experience: 5+ years of experience in data science, quantitative analytics, analytics engineering, or a related field.
Preferred Experience: Familiarity with a business intelligence tool such as Looker or Tableau, and/or experience working with go-to-market or revenue data.
Statistical Methods: Strong applied-statistics foundation, including regression, hypothesis testing, experimental design, quasi-experimental methods, and reasoning with small or imperfect samples.
Python: Proficiency using Python for analysis and modeling, including tools such as pandas, statsmodels, scikit-learn, or comparable libraries.
SQL: Advanced SQL skills, including the ability to work with complex data models, validate results, and investigate data-quality issues.
Data Platforms: Production experience with dbt or a comparable data-transformation framework, along with experience bringing data into a warehouse through APIs, Snowpipe, or comparable ingestion tools.
Business Impact: A track record of translating analysis into recommendations that influenced a business decision, operating practice, or strategic priority, along with the ability to explain technical analysis and business implications to leadership and non-technical stakeholders.