Senior Machine Learning Scientist

Faculty AI
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Scientist

Senior Machine Learning Scientist

Senior Machine Learning Scientist

Senior Machine Learning Scientist

Senior Machine Learning Scientist

Senior Machine Learning Scientist - Search

As a Senior Machine Learning Scientist, you will lead high‑impact AI projects and shape the technical direction of bespoke solutions. This role requires hands‑on technical excellence combined with crucial team leadership. You will define data science approaches, design robust software architectures, mentor junior colleagues, and ensure delivery rigour across projects all while building deep client relationships and solidifying our reputation as a leader in practical, measurable AI.


Responsibilities

  • Mapping the end‑to‑end data science approach and designing the associated software.
  • Leading project teams that deliver bespoke algorithms and high‑stakes AI solutions to clients across the sector.
  • Conceiving the core data science approach and designing the associated robust software architecture for new engagements.
  • Mentoring a small number of data scientists and supporting the professional growth of technical team members on projects.
  • Partnering with commercial teams to build client relationships and shape project scope for technical feasibility.
  • Contributing to Faculty's thought leadership and reputation through delivering courses, public speaking, or open‑source projects.
  • Ensuring best practices are followed throughout the project lifecycle to guarantee high‑quality, impactful delivery.

Qualifications

  • Senior experience in a professional data science position or a quantitative academic field.
  • Strong programming skills with fluency in Python and core libraries (NumPy, Pandas) and a deep‑learning framework such as PyTorch.
  • Deep expertise in core data science paradigms (supervised/unsupervised learning, NLP, validation) and ability to develop innovative algorithms.
  • Leadership mindset focused on growing technical capabilities of the team and nurturing a collaborative culture.
  • Commercial awareness with experience in client‑facing work and ability to translate business problems into a rigorous mathematical framework.
  • Skilled in project planning, assessing technical feasibility, estimating delivery timelines, and leading a team to deliver high‑quality work on a strict schedule.
  • Knowledge of responsible AI and ethical, reliable, cutting‑edge AI solutions for high‑stakes environments.
  • Eligibility for UK Security Clearance (SC) if working with defence clients.
  • Willingness to work between 2–4 days per week on‑site with customers, with travel across the UK; flexible to work from London office or remotely otherwise.

About Faculty

Established in 2014, Faculty has worked with over 350 global customers to transform their performance through human‑centric AI. We innovate, build and deploy responsible AI that moves the needle, and we bring unparalleled depth of technical, product and delivery expertise to clients in government, finance, retail, energy, life sciences and defence. Our business and reputation are growing fast, and we are always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology.


Benefits

  • Unlimited Annual Leave Policy
  • Private healthcare and dental
  • Enhanced parental leave
  • Family‑Friendly Flexibility & Flexible working
  • Sanctus Coaching
  • Hybrid Working (2 days in our Old Street office, London)


#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.