Senior Data Scientist

Xcede
Bristol
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist (GenAI)

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist:


If you want a job where you actively shape how a data function operates — and directly define how your own role evolves as the company grows — this is it.

Xcede has started working with a fast-growing leader in their emerging category of continuous AI auditing and independent model oversight. This AI trust & governance company want a senior data scientist to set the technical benchmarks used to assess the performance, reliability, and risk of mission-critical AI systems.


This role brings together bias assessment, robust quantitative analysis, and practical insight into how real-world evaluation processes work. You’ll likely join with deep expertise in one area and build strength across the others over time, giving you the ability to influence how methodologies are designed, outcomes are interpreted, customer guidance is delivered, and product decisions align with a rapidly changing AI accountability environment.


You’ll collaborate closely with technical leadership and those responsible for shaping the platform, contributing across in-depth investigative work, evaluation design, and strategic decision-making. Your work will raise the quality bar for analytical practice, reinforce confidence in their approach, and help define what robust, defensible AI assessment looks like in real-world use.


Requirements:

• A solid academic foundation in a relevant field

• more than five years of senior professional experience

• Strong in Python

• At ease working both in detail and in areas that are not yet fully defined

• Strong communicator

• Works effectively with others while taking clear ownership of outcomes

• Able to interpret details within their broader operational and business context

• Driven by the opportunity to make a meaningful difference through their work

• demonstrated depth across at least 2 of the following areas:

-Hands-on work evaluating and mitigating systematic risk within deployed AI models, including assessment techniques that support transparent and defensible outcomes

- Experience working with people-focused data in decision-making contexts, including evaluating outcomes, assessing differential effects, and supporting evidence-based assessment approaches

- Proven experience applying robust quantitative methods in environments where analytical decisions carry significant risk, with a strong command of statistical reasoning and repeatable analysis practices


If you are interested in this or other Data Scientist positions, please contact Gilad Sabari @ |

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 Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.