Senior Data Scientist

Propel
City of London
4 days ago
Create job alert

🚀 Lead Data Scientist, Computer Vision

📍 London, UK (Hybrid) | 🕒 Full-time


Help reinvent how the world discovers fashion.

We’re building a new era in fashion discovery — one that ignites body confidence in everybody and every body. Imagine Shazam meets Spotify, but for fashion.

Our community is tired of endless scrolling, paid ads and dead-end links. They want fashion that actually fits: in stock, in their size, and at the best price. We're creating a search experience that puts people at the centre of discovery.

As a female-founded fashion-tech startup, we're at an exciting stage — shaping something genuinely game-changing. Now we’re looking for an exceptional Lead Data Scientist (Computer Vision) to help build the intelligence at the heart of our platform.


🔍 The Role

This is a hands-on technical leadership role responsible for architecting and building the computer vision engine powering the platform’s discovery experience.

You’ll design, implement and productionise state-of-the-art models across image understanding, embeddings, object detection and emerging agentic AI systems.

You’ll work closely with the founders, product and engineering teams to shape the long-term AI and data strategy, making pragmatic technical decisions in a fast-moving startup environment.

⚠️ This role is 90% hands-on engineering. It is not a pure management role or a research-only position.


🧠 What You’ll Do

Hands-on Computer Vision Leadership

  • Design, build and deploy production computer vision and agentic AI systems powering search, recommendations and personalisation
  • Own the full data science lifecycle: problem framing → modelling → deployment → monitoring → iteration
  • Make pragmatic trade-offs between speed, quality and technical elegance

Product & User Impact

  • Translate messy real-world user problems into testable ML solutions
  • Partner closely with Product to ensure models improve user trust, confidence and discovery
  • Focus on feature value, ROI and the signals that truly matter

Data Foundations

  • Work hands-on with imperfect datasets
  • Design annotation strategies, data quality frameworks and evaluation pipelines
  • Decide where data investment matters most

Technical Direction & MLOps

  • Establish pragmatic MLOps practices (deployment, monitoring, iteration)
  • Build scalable but lightweight ML pipelines in AWS alongside engineering
  • Ensure models are robust, reliable and safe for production

Team & Culture

  • Set the technical bar for data science
  • Mentor future data scientists as the team grows
  • Bring curiosity, humility and ownership to a high-ambiguity startup environment


✅ What We’re Looking For

  • Bachelor’s or Master’s in Computer Science, Mathematics or related field
  • Strong computer vision expertise and experience fine-tuning state-of-the-art models
  • Deep learning experience (e.g. PyTorch, CUDA, model optimisation for training and inference)
  • Strong MLOps and engineering mindset (CI/CD, automated deployment, monitoring)
  • Solid understanding of data engineering, data quality and annotation strategies
  • Comfortable working in fast-paced startup environments
  • A simplicity-first mindset — only introduce complexity when it’s needed
  • Excellent communication skills across technical and non-technical audiences


🌟 Why Join?

  • Build the core AI engine of a breakthrough fashion discovery platform
  • Work directly with founders on product and technical strategy
  • Join a mission-driven, female-founded startup at a defining growth stage
  • Shape a product designed to make fashion discovery more inclusive, empowering and intelligent


đź’Ś Interested?

We’d love to hear from exceptional builders who want to shape the future of fashion discovery.

Apply via LinkedIn or reach out directly.

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

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 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.

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.

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.