Customer Facing Machine Learning Scientist

Skills Alliance
London
7 months ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Scientist, Borrowing

Lead Data Scientist

Forward-Deployed Data Scientist II

Data Science Manager

Data Scientist

Data Scientist

Requirements

Essential Qualifications:

  • MSc in Computational Science or Machine Learning with a focus on Computational Biology (PhD preferred).
  • Proven experience in biological data curation, organization, and management.
  • Deep understanding of computational biology tools, methodologies, and best practices.
  • Solid foundation in machine learning techniques and tools, with an emphasis on real-world applications.
  • Proficient in Python programming.
  • Familiarity with data processing pipelines and cloud-based computing environments.
  • Strong communication skills with the ability to convey complex scientific concepts clearly.
  • A customer-focused mindset with an interest in building impactful products.
  • Genuine passion for the intersection of AI and Biology.

Preferred Qualifications:

  • Industry experience in biotechnology or pharmaceuticals, especially in internal or client-facing roles.
  • Contributions to open-source projects in computational biology.
  • Familiarity with advanced ML techniques such as Transformer architectures, Structured State-Space Models, or Bayesian Optimization.
  • Track record of scientific publications in machine learning or computational biology.
  • Experience in therapeutic or diagnostic development program

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.