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For Companies At a Glance
- Tasks: Design advanced data science challenges and create rigorous technical solutions.
- Company: Alignerr, partnering with top AI research teams globally.
- Benefits: Flexible remote work, competitive pay, and opportunities for contract renewals.
- Why this job: Engage directly 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 30 - 60 ÂŁ per hour.
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.
Location: Remote
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 Glasgow 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 Glasgow
✨Tip Number 1
Get your networking game on! Reach out to professionals in the AI and data science fields through LinkedIn or relevant forums. We can leverage our connections to find out about hidden job opportunities that might not even be advertised.
✨Tip Number 2
Prepare for those interviews like a pro! Brush up on your technical skills and be ready to discuss complex problems, like hyperparameter optimisation or Bayesian inference. We should practice explaining our thought process clearly, as communication is key in this field.
✨Tip Number 3
Showcase your projects! Whether it's a GitHub repository or a personal website, we need to display our work. Highlight any advanced data science challenges you've tackled, especially if they relate to the job description. This will make us stand out!
✨Tip Number 4
Don’t forget to apply through our website! It’s quick and easy, and our team reviews applications daily. Completing the AI interview and application steps promptly can give us an edge over other candidates.
We think you need these skills to ace Data Scientist (Masters) - AI Data Trainer in Glasgow
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 fits into the AI training world, so don’t hold back on showcasing your analytical writing and technical expertise!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about AI and data science. We love seeing candidates who can communicate complex ideas clearly, so make sure to demonstrate your ability to break down technical concepts.
Showcase Your Projects: If you've worked on any relevant projects, whether in school or on your own, include them in your application. We’re keen to see your hands-on experience with data science challenges, especially those involving Python, R, or SQL.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, it only takes 15-20 minutes, so why not?
How to prepare for a job interview at Alignerr
✨Know Your Data Science Stuff
Make sure you brush up on your knowledge of advanced data science concepts like hyperparameter optimisation and Bayesian inference. Be ready to discuss how you've applied these in real-world scenarios, as this will show your depth of understanding.
✨Show Off Your Coding Skills
Prepare to demonstrate your coding abilities in Python or R. You might be asked to write scripts or SQL queries on the spot, so practice common tasks and be familiar with libraries like scikit-learn and TensorFlow.
✨Communicate Clearly
Since analytical writing is key for this role, practice explaining complex technical concepts in simple terms. You might be asked to present your thought process during the interview, so clarity is crucial!
✨Attention to Detail is Key
Be prepared to discuss how you ensure accuracy in your work. Whether it's checking code syntax or validating statistical conclusions, having examples ready will highlight your meticulous nature and fit for the role.
