▷ (Urgent) Data Analyst...

DataAnnotation

Lichfield

6 months ago

Applications closed

Job Description

DataAnnotation is committed to creating quality AI. Join our team to help train AI chatbots while gaining the flexibility of remote work and choosing your own schedule.

We are looking for a proficient Data Analyst to join our team to train our AI chatbots to code. You will work with the chatbots that we are building in order to measure their progress, as well as write and evaluate code.

To apply to this role, you will need to be proficient in either Python and/or JavaScript. However, all of the following programming languages are also relevant: TypeScript, C, C#, C++, HTML/CSS, React, Go, Java, Kotlin, SQL, and Swift in order to solve coding problems (think LeetCode, HackerRank, etc). For each coding problem, you must be able to explain how your solution solves the problem.

Benefits:

This is a full-time or part-time REMOTE position
You’ll be able to choose which projects you want to work on
You can work on your own schedule
Projects are paid hourly, starting at $40+ USD per hour, with bonuses for high-quality and high-volume work

Responsibilities:
Come up with diverse problems and solutions for a coding chatbot
Write high-quality answers and code snippets
Evaluate code quality produced by AI models for correctness and performance

Qualifications:
Fluency in English (native or bilingual level)
Proficient in either Python and/or JavaScript
Excellent writing and grammar skills
A bachelor's degree (completed or in progress)
Previous experience as a Software Developer, Coder, Software Engineer, or Programmer

Note: Payment is made via PayPal. We will never ask for any money from you. PayPal will handle any currency conversions from USD. This job is only available to those in the US, UK, Canada, Australia, or New Zealand. Those located outside of these countries will not see work or assessments available on our site at this time.

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

Industry Insights

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

Feb 10, 2026

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Feb 3, 2026

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Jan 23, 2026

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.