Senior Software Engineer, Data

fal

San Francisco (CA)

On-site

USD 180,000 - 225,000

Full time

14 days+

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Benefits offered by this job

Health, dental, and vision insurance

Relocation assistance

Regular team events and offsites

Job summary

fal is seeking a Senior Data Scientist for Go-to-Market in San Francisco. This role involves owning metrics that drive sales performance, with a focus on deep analytics and working closely with sales teams. Candidates should have 5+ years in data science, strong SQL and Python proficiency, and familiarity with Salesforce data. The compensation ranges from $180,000 to $225,000 plus equity and benefits. Remote work is considered for Senior and Staff levels.

Qualifications

5+ years of experience in data science or analytics roles, with 3+ years in GTM, RevOps, or sales analytics.
Strong proficiency with SQL and Python for analysis, modeling, and experimentation.
Deep familiarity with Salesforce data and the operational rhythms of an enterprise sales organization.

Responsibilities

Own metrics and reporting to drive sales execution at fal.
Partner with sales leadership to translate strategic questions into measurement frameworks.
Build data foundations for first-class GTM analytics.

Skills

Advanced SQL

Proficiency in Python

Data analysis

Salesforce data familiarity

Embedded analytics partnership

Tools

dbt

Modern analytics stacks

Job description

fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products.

As generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on.

About this role:

As a Senior Data Scientist for Go-to-Market at fal, you will be the analytical backbone of our revenue organization. Embedded directly with the GTM function, you will own the metrics that tell us how our pipeline is performing, how our sales team is executing, and where the next dollar of revenue is most likely to come from.

This is a high‑leverage, high‑visibility role. GTM leadership will look to you for the answers - on rep performance, quota attainment, pipeline health, AM coverage, and segment economics - and you'll shape both the questions we ask and the systems we build to answer them. You'll partner closely with Product Intelligence and Data Engineering as part of fal's center‑of‑excellence data team, while acting as the embedded specialist for everything GTM.

Key responsibilities:

Own the metrics and reporting that drive sales execution at fal: pipeline health, quota attainment, conversion rates, sales cycle, AM coverage, and segment‑level economics.
Partner directly with the sales leadership and rev‑ops to translate strategic questions into measurement frameworks and weekly operating cadences.
Build the data foundations that make GTM analytics first‑class: account‑manager tracking, territory and quota models, opportunity‑stage instrumentation, and rep‑level performance views.
Run rigorous deep‑dives - win/loss, lead source effectiveness, segmentation, expansion drivers - that change how GTM allocates time and headcount.
Shape the CRM, internal tooling, and downstream data models that everyone in GTM depends on, and set the standards for how GTM data is captured, joined, and trusted.

Requirements:

5+ years of experience in data science or analytics roles, with at least 3+ years specifically in GTM, RevOps, or sales analytics at an enterprise or B2B SaaS company. This is a hard requirement - we are looking for someone who has seen many variations of this problem and can run with it.
Advanced SQL and proficiency in Python for analytics, modeling, and experimentation.
Deep familiarity with Salesforce data, pipeline mechanics, quota and territory models, and the operational rhythms of an enterprise sales org.
Strong proficiency with dbt and modern analytics stacks; comfort partnering with data engineering on production‑grade pipelines.
Proven ability to operate as an embedded analytics partner - building trust with sales leadership and translating ambiguous strategic asks into clear measurement.
Demonstrated bias for action; you set up the dashboard, write the SQL, and ship the framework yourself when needed.
A track record of shipping data products that change behavior, not just dashboards that get viewed once.

Nice to have: