Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Design and evaluate advanced AI models while documenting challenges and solutions.
- Company: Join Alignerr, a leader in AI research collaboration.
- Benefits: Competitive pay, remote work flexibility, and opportunities for growth.
- Why this job: Make an impact on cutting-edge AI technology and enhance your data science skills.
- Qualifications: Masters or PhD in Data Science or related field; strong analytical skills required.
- Other info: Dynamic role with high agency and international reach.
The predicted salary is between 26 - 52 ÂŁ per hour.
Alignerr, Cambridge, England, United Kingdom
Base pay range: $40.00/hr – $80.00/hr
Location: Remote. Hourly contract, 10–40 hours/week.
About The Job
At Alignerr, we partner with the world’s leading AI research teams and labs to build and train cutting‑edge AI models. You’ll challenge advanced language models on topics such as machine learning theory, statistical inference, neural network architectures, and data engineering pipelines, documenting every failure mode so we can harden model reasoning.
What You’ll Do
- Design advanced data science challenges across domains such as hyperparameter optimization, Bayesian inference, cross‑validation strategies, and dimensionality reduction.
- Create rigorous, step‑by‑step technical solutions—including Python/R scripts, SQL queries, and mathematical derivations—that serve as "golden responses".
- Evaluate AI‑generated code (using libraries such as Scikit‑Learn, PyTorch, or TensorFlow), data visualizations, and statistical summaries for technical accuracy and efficiency.
- Identify logical fallacies in AI reasoning—such as data leakage, overfitting, or improper handling of imbalanced datasets—and provide structured feedback to improve the model’s "thinking" process.
Requirements
- Advanced degree: Masters (pursuing or completed) or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a heavy emphasis on data analysis.
- Domain expertise in supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP.
- Excellent analytical writing skills to communicate highly technical algorithmic concepts and statistical results clearly and concisely.
- High attention to detail when checking code syntax, mathematical notation, and statistical conclusions.
- No AI experience required.
Preferred
- Prior experience with data annotation, data quality, or evaluation systems.
- Proficiency in production‑level data science workflows (e.g., MLOps, CI/CD for models).
Why Join Us
- Excellent compensation with location‑independent flexibility.
- Direct engagement with industry‑leading LLMs.
- Contractor advantages: high agency, agility, and international reach.
- More opportunities for contracting renewals.
Application Process
- Submit your resume.
- Complete a short screening.
- Project matching and onboarding.
PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
Data Scientist (Masters) - AI Data Trainer in Cambridge employer: Alignerr
Contact Detail:
Alignerr Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist (Masters) - AI Data Trainer in Cambridge
✨Tip Number 1
Network like a pro! Reach out to folks in the data science community, especially those who work with AI. Join relevant forums or LinkedIn groups and don’t be shy about asking for advice or insights.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving machine learning or data analysis. This is your chance to demonstrate your expertise beyond just a CV.
✨Tip Number 3
Prepare for interviews by brushing up on technical concepts and coding challenges. Practice explaining complex ideas clearly, as communication is key in data science roles.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, you’ll be part of a team that values innovation and flexibility in the data science field.
We think you need these skills to ace Data Scientist (Masters) - AI Data Trainer in Cambridge
Some tips for your application 🫡
Tailor Your Resume: Make sure your resume highlights your relevant skills and experiences that align with the Data Scientist role. We want to see how your background in data science, machine learning, or any related field makes you a perfect fit for us!
Show Off Your Analytical Writing Skills: Since excellent analytical writing is key for this position, include examples of your technical writing in your application. Whether it's a project report or a research paper, we love seeing how you communicate complex ideas clearly.
Be Detail-Oriented: Pay close attention to your application details. Check for typos, ensure your code snippets are correct, and make sure your mathematical notation is spot on. We appreciate candidates who take the time to double-check their work!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands quickly. Plus, it shows us you're keen on joining our team at Alignerr!
How to prepare for a job interview at Alignerr
✨Know Your Stuff
Make sure you brush up on your knowledge of machine learning theory, statistical inference, and neural network architectures. Be ready to discuss specific projects or challenges you've tackled in these areas, as this will show your expertise and passion for data science.
✨Showcase Your Analytical Skills
Prepare to demonstrate your analytical writing skills by explaining complex algorithmic concepts clearly. You might be asked to break down a technical problem or explain your thought process behind a solution, so practice articulating your ideas concisely.
✨Get Hands-On with Tools
Familiarise yourself with the tools mentioned in the job description, like Python, R, SQL, and libraries such as Scikit-Learn, PyTorch, or TensorFlow. If possible, work on a small project using these tools to showcase your practical experience during the interview.
✨Prepare for Problem-Solving Questions
Expect to face questions that test your problem-solving abilities, especially around data leakage, overfitting, and imbalanced datasets. Think of examples where you've identified and resolved these issues, and be ready to discuss your approach in detail.
