# Data Scientist - AI Safety

**Company:** [ElevenLabs](https://hotfix.jobs/companies/elevenlabs)
**Location:** London, United Kingdom
**Role:** Data Science
**Skills:** Python, SQL, Machine Learning, Data Labeling, Dataset Versioning, Evaluation Frameworks, Data Quality Control, Ml Pipelines, Ai Safety, Content Moderation
**Posted:** 2026-09-01

> Own the datasets and evaluation workflows supporting AI safety, from collection and labeling through production model assessment. The role requires practical Python and SQL skills, strong dataset judgment, and cross-functional collaboration with policy, engineering, and research teams.

## Job Description

## Responsibilities
- Own safety datasets end-to-end, including collection, cleaning, sampling, labeling, quality control, versioning, and readiness for training and evaluation.
- Translate safety policy into clear, consistent labeling and evaluation criteria with policy specialists.
- Design and manage labeling processes, including sourcing, onboarding, and overseeing external contributors.
- Build evaluation workflows for production models using real-world data to track performance and surface issues.
- Develop lightweight Python and SQL pipelines and tooling to make data work faster and reproducible.
- Partner with ML engineers to define training-ready data requirements.

## Requirements
- Experience as a Data Scientist or in a similar role working with real-world datasets and ML models.
- Strong understanding of dataset collection, cleaning, sampling, labeling, quality control, and evaluation.
- Proficiency with Python and SQL for analysis and practical data workflows.
- Strong critical thinking and judgment, with the ability to handle ambiguity and translate complex guidelines into consistent, scalable decisions.
- Ability to work autonomously and communicate clearly across policy, engineering, and research teams.

## Nice-to-haves
- Experience in AI safety, trust and safety, content moderation, or fraud and abuse detection.
- Experience building datasets or evaluation frameworks, or working with adversarial or safety-focused machine learning.
- Experience managing human-in-the-loop labeling, dataset versioning, or ML pipeline tooling.
- Experience operationalizing guidelines with policy or legal teams.

## Benefits
- Annual discretionary professional development stipend.
- Annual discretionary social travel stipend.
- Annual company offsite.
- Monthly coworking stipend for employees outside main hubs.

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