Data Science Subject Matter Expert - AI Evaluation (UK-Remote)

Braintrust
Edinburgh
3 weeks ago
Applications closed

Related Jobs

View all jobs

Workforce Data Analyst

Senior Data Scientist SME & AI Architect

Data Analyst Trainer

Technical Associate II, MSAT (Data Scientist)

Senior Data Scientist

Senior Data Scientist

Data Science Subject Matter Expert - AI Evaluation (UK-Remote)

16 hours ago Be among the first 25 applicants


This range is provided by Braintrust. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.


Base pay range

$75.00/hr - $90.00/hr


Job Description:


Seeking multiple Data Science Subject Matter Experts to help design, run, and optimize data collection and evaluation workflows for GenAI research.


You’ll translate high-level research needs into scalable processes, produce and curate challenging domain problems, and ensure factual, bias-aware, high-quality datasets for LLM training.



  • To Note: This is for an immediate project need. Project is approved for 3-months initially, with possibility to extend based on project/client demands.
  • To Note: Hourly rate range (75 - 90) is in USD per hour

Responsibilities:



  • Partner with GenAI researchers/engineers to capture data needs and success criteria.
  • Expand high-level requirements into clear, executable workflows for larger teams.
  • Execute collection/evaluation workflows rapidly with minimal supervision.
  • Innovate on workflows to maximize throughput and quality.
  • Collaborate cross-functionally to maintain quality at scale.
  • Conduct in-depth LLM-assisted research; gather reliable, up-to-date info.
  • Craft original, high-quality content and hard problems for LLM eval/train.
  • Perform rigorous fact-checking (precision/recall) to prevent misinformation.

Requirements:



  • Education: Master’s with distinction or PhD in Data Science; top-tier institution preferred. Significant domain experience considered.
  • Detail orientation; precise data presentation; thorough proofreading.
  • Communication: articulate complex info; strong collaboration.
  • Understanding of AI/LLMs, their capabilities/limits.
  • Prompt engineering and familiarity with AI writing tools.
  • Ethical AI awareness and data literacy (collection, cleaning, transformation).
  • Thrives in fast-paced, minimally supervised environments.

Seniority level
  • Entry level

Employment type
  • Full-time

Job function
  • Engineering, Information Technology, and Science

Industries
  • Technology, Information and Internet, Data Infrastructure and Analytics, and IT System Data Services


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.