Lead Data Scientist - Remote

Hermiston
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist - Deep Learning Practitioner

Lead Data Scientist - Deep Learning Practitioner

Lead Data Scientist

Our client is building the most advanced AI platform in their market. They help their clients serve customers with unmatched speed and accuracy.
They’ve invested heavily into building the ML stack, partnered with leading universities, and trained models on millions of expert-tagged images. Now, they’re scaling globally — and need a world-class Lead Data Scientist to help push the boundaries of computer vision, video analysis, and multimodal LLMs while solving real-world challenges.
Role Overview
They are looking for an experienced Lead Data Scientist to spearhead machine-learning initiatives, with particular focus on computer vision, large language models, and production ready ML pipelines in Azure. You will act as the technical lead for the team, setting direction, guiding best practices, and ensuring the successful delivery of high-impact AI solutions.
Key Responsibilities
· Develop, train, and deploy computer vision models (object detection, image classification, segmentation, multi-modal learning)
· Fine-tune, evaluate, and productionise multi-modal LLMs for business applications.
· Drive experimentation and prototyping of advanced ML/AI techniques
· Provide technical direction, mentoring, and hands-on guidance to the data science team.
· Work with engineering, product, and business stakeholders to align ML strategy with business goals.
· Architect and productionise end-to-end ML pipelines on Azure, while ensuring scalability, reproducibility, and monitoring of deployed models.
Requirements
· 6+ years in data science / ML, with at least 2 years in a technical lead role.
· Deep experience in training and deploying computer vision models into production
· Proven track record with LLM fine-tuning, prompt engineering and productionisation
· Deep experience in MLOps on Azure, including CI/CD, monitoring and scaling pipelines.
· Strong coding skills in Python, with frameworks such as PyTorch, FastAPI and Azure CLI.
ALL APPLICANTS MUST BE FREE TO WORK IN THE UK
Exposed Solutions is acting as an employment agency to this client.
Please note that no terminology in this advert is intended to discriminate on any grounds, and we confirm that we will gladly accept applications from any person for this role

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.

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.

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.

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.