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- Tasks: Develop and challenge advanced AI models while documenting failures to enhance model reasoning.
- Company: Join Alignerr, a leader in AI research collaboration.
- Benefits: Earn $40-$80/hr with flexible remote work and opportunities for growth.
- Why this job: Make an impact on cutting-edge AI technology and work with industry leaders.
- Qualifications: Master’s or PhD in Data Science or related field; strong analytical skills required.
- Other info: Enjoy high agency and agility in a dynamic, international environment.
The predicted salary is between 30 - 60 ÂŁ per hour.
Join to apply for the Data Scientist (Masters) - AI Data Trainer role at Alignerr.
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 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 employer: Alignerr
Contact Detail:
Alignerr Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist (Masters) - AI Data Trainer
✨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 on the role.
✨Tip Number 2
Prepare for technical interviews by brushing up on your coding skills and understanding of machine learning concepts. Practice explaining complex topics clearly, as you’ll need to demonstrate your analytical writing skills during discussions.
✨Tip Number 3
Showcase your projects! If you’ve worked on any data science challenges or AI models, make sure to have them ready to discuss. Highlight your problem-solving process and the impact of your work—this will impress potential employers.
✨Tip Number 4
Apply through our website! It’s quick and easy, and we review applications daily. Completing your AI interview and application steps promptly can give you an edge over other candidates.
We think you need these skills to ace Data Scientist (Masters) - AI Data Trainer
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 analysis and machine learning fits into what we do at Alignerr.
Show Off Your Writing Skills: Since analytical writing is key for this position, include examples of your technical writing. We love clear and concise communication, so don’t shy away from showcasing your ability to explain complex concepts.
Be Detail-Oriented: Pay close attention to your application details. We appreciate precision, so double-check your code syntax and any statistical conclusions you mention. It reflects your attention to detail, which is crucial for this role.
Apply Through Our Website: We encourage you to apply directly through our website. It streamlines the process for us and ensures your application gets the attention it deserves. Plus, it only takes 15-20 minutes!
How to prepare for a job interview at Alignerr
✨Know Your Data Science Stuff
Make sure you brush up on your knowledge of machine learning theory, statistical inference, and neural networks. Be ready to discuss specific projects or challenges you've tackled in these areas, as this will show your depth of understanding.
✨Show Off Your Analytical Writing Skills
Since the role requires clear communication of complex concepts, prepare to demonstrate your analytical writing skills. Bring examples of your previous work where you've explained technical topics concisely and effectively.
✨Prepare for Technical Audits
Expect to evaluate AI-generated code and data visualisations during the interview. Familiarise yourself with libraries like Scikit-Learn, PyTorch, and TensorFlow, and be ready to discuss how you would assess their accuracy and efficiency.
✨Attention to Detail is Key
Highlight your meticulous nature by discussing how you ensure precision in your work. Prepare to talk about instances where your attention to detail made a significant difference in your projects, especially regarding code syntax and statistical conclusions.
