# Data Scientist at Higgsfield

- Company: Higgsfield
- What the company does: Create images, videos, and voice content with Higgsfield AI from text prompts or references. Edit and upscale media, automate creative workflows with its AI agent, and generate content on web and mobile. Backed by Accel and Menlo.
- Company website: https://higgsfield.ai/
- Type: Startups (AI role)
- Level: Mid level
- Location: Almaty, Kazakhstan
- Work setup: On-site
- Posted: 2026-09-07
- Apply by: 2026-10-22
- Apply: https://jobs.ashbyhq.com/higgsfieldai/a1c3bffb-7243-45f8-ace6-ceb89239c475
- Page: https://www.1752.vc/careers/jobs/higgsfield-data-scientist/

## About the role

Higgsfield AI is the fastest-scaling generative AI company in history, hitting $500M in annual revenue run rate, 25M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like.

## What they're looking for

- Experience shipping models that changed what the business did to real users — a targeted campaign, a pricing rule, a retention programme. Notebooks and offline benchmarks are not this
- Depth in at least two of: churn and propensity modelling, uplift & causal ML, LTV and subscription economics, experimentation at scale
- Genuine causal literacy: randomized holdouts, incrementality, Qini and uplift curves, selection effects, and why a lift measured before-and-after is usually not a lift
- Strong SQL and Python: cohorts, funnels and retention on raw event data without help, gradient boosting, and the boring parts — feature pipelines, retraining cadence, a scoring job that runs on a schedule and doesn't rot
- Understanding of subscription plus one-time purchase mechanics: recurring vs one-off revenue, refunds, plan changes, the deferred value of unspent credits, and why revenue and margin disagree about the same customer
- Commercial judgment: you name the decision and the metric before you pick the model, and you know when the answer is a rule rather than a model

Tags: Engineering & Product