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For Companies At a Glance
- Tasks: Design and solve advanced data science challenges while training cutting-edge AI models.
- Company: Join Alignerr, a leader in AI research collaboration.
- Benefits: Earn $40-$80/hr with flexible remote work options.
- Why this job: Make an impact on AI development and enhance your data science skills.
- Qualifications: Master’s or PhD in Data Science or related field; strong analytical skills required.
- Other info: Enjoy contractor flexibility and opportunities for renewals.
The predicted salary is between 30 - 60 ÂŁ per hour.
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 like 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
- Develop complex problems: design advanced data science challenges across domains such as hyperparameter optimization, Bayesian inference, cross‑validation strategies, and dimensionality reduction.
- Author ground‑truth solutions: create rigorous, step‑by‑step technical solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as “golden responses.”
- Technical auditing: evaluate AI‑generated code (using libraries like Scikit‑Learn, PyTorch, or TensorFlow), data visualizations, and statistical summaries for technical accuracy and efficiency.
- Refine reasoning: 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: Master’s (pursuing or completed) or PhD in Data Science, Statistics, Computer Science, or a quantitative field with a heavy emphasis on data analysis.
- Domain expertise: strong foundational knowledge in core areas such as supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP.
- Analytical writing: ability to communicate highly technical algorithmic concepts and statistical results clearly and concisely in written form.
- Attention to detail: high level of precision when checking code syntax, mathematical notation, and the validity of 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 (takes 15‑20 min)
- 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 London 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 London
✨Tip Number 1
Get your networking game on! Connect with professionals in the AI and data science fields on platforms like LinkedIn. Join relevant groups, participate in discussions, and don’t hesitate to reach out for informational chats. You never know who might have a lead on that perfect role!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to machine learning and data analysis. Use GitHub or a personal website to display your work. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews like a pro! Research common data science interview questions and practice your answers. Be ready to discuss your thought process on problem-solving and how you approach complex data challenges. Confidence is key, so rehearse until you feel comfortable!
✨Tip Number 4
Don’t just apply anywhere—apply through our website! We’re always on the lookout for talented individuals like you. Tailor your application to highlight your relevant experience and skills, and make sure to follow up after submitting to show your enthusiasm for the role.
We think you need these skills to ace Data Scientist (Masters) - AI Data Trainer in London
Some tips for your application 🫡
Tailor Your Resume: Make sure your resume highlights the skills and experiences that align with the Data Scientist role. Use keywords from the job description to show us you’re a perfect fit!
Show Off Your Analytical Writing: Since we value clear communication, include examples of your analytical writing. Whether it’s reports or documentation, let us see how you can explain complex concepts simply.
Be Detail-Oriented: Pay close attention to your application details. Double-check for any typos or errors in your resume and cover letter. We love precision, especially when it comes to data!
Apply Through Our Website: For the best chance of getting noticed, apply directly through our website. It’s quick and easy, and we review applications daily, so don’t wait too long!
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 these topics in detail, as they are crucial for the role at Alignerr.
✨Showcase Your Problem-Solving Skills
Prepare to tackle complex data science challenges during the interview. Think about how you would approach hyperparameter optimization or dimensionality reduction, and be ready to explain your thought process clearly.
✨Communicate Clearly
Since analytical writing is key for this position, practice explaining technical concepts in a straightforward manner. You might be asked to describe your previous work or solutions, so clarity is essential.
✨Attention to Detail is Key
Demonstrate your precision by discussing how you ensure accuracy in your code and statistical conclusions. Bring examples of how you've caught errors in your past work, as this will show your commitment to quality.
