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For Companies At a Glance
- Tasks: Design and develop advanced data science challenges while training cutting-edge AI models.
- Company: Join Alignerr, a leader in AI research collaboration.
- Benefits: Competitive pay, remote work flexibility, and opportunities for growth.
- Why this job: Engage with industry-leading AI models and make a real impact.
- 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.
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
- Develop Complex Problems: Design advanced data science challenges across domains like 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: 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: Strong foundational knowledge in core areas such as supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP.
- Analytical Writing: The 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 Manchester 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 Manchester
✨Tip Number 1
Network like a pro! Reach out to folks in the data science community, especially those who work with AI. Join relevant groups on LinkedIn or attend meetups to make connections that could lead to job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving machine learning and data analysis. This is your chance to demonstrate your expertise and problem-solving abilities to potential employers.
✨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. We recommend mock interviews to build confidence!
✨Tip Number 4
Don’t forget to apply through our website! It’s quick and easy, and we review applications daily. Make sure to complete your AI interview and application steps to increase your chances of landing that dream job.
We think you need these skills to ace Data Scientist (Masters) - AI Data Trainer in Manchester
Some tips for your application 🫡
Craft a Tailored Resume: Make sure your resume highlights your relevant experience in data science and AI. We want to see how your skills align with the job description, so don’t be shy about showcasing your projects and achievements!
Show Off Your Analytical Writing Skills: Since clear communication is key for this role, include examples of your analytical writing. Whether it’s a report or a blog post, we love seeing how you can break down complex concepts into digestible bits.
Be Detail-Oriented: Double-check your application for any typos or errors. We appreciate precision, especially when it comes to technical details, so make sure everything is spot on before hitting submit!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the quickest way for us to get your application and start the review process. Plus, it helps us keep everything organised!
How to prepare for a job interview at Alignerr
✨Know Your Stuff
Make sure you brush up on your data science fundamentals, especially in areas like machine learning theory and statistical inference. Be ready to discuss complex problems and solutions you've worked on, as this will show your depth of knowledge.
✨Showcase Your Analytical Writing
Since the role requires clear communication of technical concepts, prepare to demonstrate your analytical writing skills. Bring examples of your previous work where you've explained complex algorithms or statistical results in a straightforward manner.
✨Prepare for Technical Audits
Familiarise yourself with evaluating AI-generated code and data visualisations. You might be asked to critique some code during the interview, so practice explaining your thought process and how you would ensure technical accuracy and efficiency.
✨Attention to Detail is Key
Highlight your meticulous nature by discussing how you check code syntax and validate statistical conclusions. Consider sharing specific instances where your attention to detail made a significant difference in your work.
