Senior Software and Data Engineer

Portman Scott
London
1 year ago
Applications closed

Related Jobs

View all jobs

Lead/Senior Data Scientist - Ad Tech Locational Data

Lead/Senior Data Scientist - Ad Tech Locational Data

Senior Simulation Engineer (Data Science)

Senior Data Scientist - QuantumBlack Labs

Senior Data Scientist

Applied AI and Machine Learning Scientist - Senior Associate

PermanentSalary - £80,000 - £95,000 p/a + benefitsRemote, UKData plays a crucial role in guiding my clients investment decisions and managing risk. Their analytics provide insights that help them evaluate growth potential and performance, while separately allowing them to support their clients. Through a strong data-driven approach, they enable entrepreneurs to make impactful, data-informed decisions that support sustained growth.TasksI am seeking a resourceful and adaptable engineer with 3-5 years of experience and a strong foundation in computer science, data science, and mathematics. This role covers analytics, platform, architecture, and data engineering and is ideal for a versatile individual eager to take a hands-on role with the autonomy to drive projects and make significant contributions as we develop our capabilities from the ground up.RequirementsQualifications:Education: BSc, MSc, or PhD in Computer Science, Data Science, Applied Mathematics, or a related field.Experience: 3-5 years in a FinTech, data science, or data engineering role with a strong focus on independent project ownership and end-to-end solution development. Experience in a start-up environment is desirable.Technical Skills:Advanced skills in Python, Django, or similar programming languages, with a strong command of data processing and machine learning libraries.MUST HAVE - Proficiency in data visualisation tools (e.g., Matplotlib, Plotly, Tableau) for effective data presentation.Familiarity with cloud services (preferably Google Cloud) and an ability to leverage available resources creatively.If you meet these qualifications and are excited about the opportunity to join our team, we’d love to hear from you!#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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.