Data Science Expert - AI Content Specialist

Alignerr
Glasgow
3 weeks ago
Applications closed

Related Jobs

View all jobs

Data Science Consultant

Principal, AI Data Science

Data Scientist / Statistician (Model Developer)

Data Scientist / Statistician (Model Developer)

Data Scientist / Statistician (Model Developer)

Lead Data Scientist / Deep Learning Practitioner

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


Organization

Alignerr


Position

Data Science Expert - AI Content Specialist


Type

Hourly Contract


Compensation

US$40–$80 /hour


Commitment

10–40 hours/week


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: 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.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.