Data Scientist

Broadbean Technology
Greater London
6 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Contract Data Scientists - AWS, Python, AI Engineering

Rate: £500/day
IR35: Inside
Location: Remote (Must be UK Based)

Duration: 6 Weeks Discovery Piece of Work.

We're working with a high-profile public sector programme undergoing a significant data and digital transformation. They're seeking experienced Data Scientists to join their growing cloud and analytics function. This role will be central to building advanced data products, supporting AI/ML initiatives, and ensuring scalable, secure delivery of data-driven solutions in a highly regulated environment.

Role Overview

You'll be part of a multidisciplinary data team, working at the intersection of data science, engineering, and cloud infrastructure. The environment is delivery-focused, with close collaboration across data engineering, AI, and platform teams. The ideal candidate is an experienced data scientist with a strong AWS background, comfortable delivering production-ready models, and with exposure to AI engineering practices such as model deployment and MLOps.

Key Responsibilities

  • Develop and deploy machine learning models and analytical solutions within AWS

  • Collaborate with data engineers to build scalable data pipelines and feature stores

  • Apply modern statistical, predictive, and AI/ML techniques to complex datasets

  • Support model deployment and monitoring using MLOps best practices

  • Contribute to the development of cloud-first, secure, and scalable data solutions

  • Work closely with stakeholders to translate requirements into data-driven outcomes

  • Support knowledge-sharing and champion data science best practice within the team

Required Experience

  • Strong AWS experience (SageMaker, Lambda, ECS, API Gateway, S3, etc.)

  • Proven expertise with Python for data science, ML, and automation

  • Experience delivering ML models into production environments

  • Exposure to AI engineering concepts (MLOps, containerisation, CI/CD for ML)

  • Strong applied statistical and machine learning knowledge

  • Experience collaborating within multidisciplinary teams in agile environments

Apply now or email for more information

ZG9tLjI1NzkwLjEyMjcxQGJyaW9kaWdpdGFsLmFwbGl0cmFrLmNvbQ.gif

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.