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# Sr. Data Scientist, Forward-Deployed

- **Company:** 6 Sense Insights, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $168,350.0 - $230,913.0
- **Contract:** Permanent contract
- **Skills:** Software as a Service, Customer Data Management, Apache Hive, Python (Programming Language), Standard Sql, Tableau (Software), Value Engineering, Snowflake, Data Management
- **Published:** October 27, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0232632c2562d6ff

## About the Role

Sr. (5+ years) - measures engagement impact and works on customer models, * Required: 5+ years in data science or advanced analytics, with experience building models and measuring business outcomes.

 * Strong SQL and Python, and experience turning messy data into analysis people trust, including matching records across systems by ID.
 * Solid grounding in statistics and experiment design: baselines, control groups, and knowing when a difference is real.
 * Experience building dashboards or reporting for business stakeholders.
 * Clear communicator who can explain what the numbers mean, and what they don't.
 * Comfortable switching between several projects at once.
 * Nice to have: B2B go-to-market or SaaS customer data (CRM, renewals, pipeline, customer health); a customer-facing, consulting or analytics-for-clients role; BI tools such as Tableau; data platforms such as SingleStore, Snowflake or Hive.

## Description

We're hiring a Sr. Data Scientist to show the impact our work has for customers. You'll measure how each engagement moves customers' pipeline and renewals, build the value analysis customers and leadership rely on, and work hands-on with the senior data scientists on customer models.

This role sits where analytics meets modeling. It suits someone who wants to see the business outcome of their work and grow into a forward-deployed data scientist.

What You'll Do

Build for the customer

 * Set a baseline for every engagement before work starts, then track the results: account health, renewal outcome, and how predictive scores perform for the customer's sales team.
 * Measure how scored accounts perform against unscored ones: close rate, sales cycle length and deal size.
 * Assess the impact of model and configuration changes on model performance, qualified-account volume and pipeline timing.
 * Run before-and-after and holdout-style analyses that separate a change's real effect from noise.
 * Work on live engagements with the senior data scientists: validate account lists, compare a customer's CRM fields with 6sense firmographic data, and help tailor and tune models.

Productize for everyone

 * Build value analysis Data Science can run for any customer on demand, such as qualified-account efficiency, deal journeys and conversion by score bucket.
 * Build the dashboards and reporting the team, Customer Success and leadership use to follow engagement outcomes.