Senior Data Scientist

Kleboe Jardine Ltd
Birmingham
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist (Document Search)

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

My client is a successful multi-domain data consultancy business headquartered inEdinburghand operating with offices in bothLondonandBristol. The business is enjoying sustained growth.


Their practice brings together experts across key business sectors including Healthcare & Pharmaceuticals, Retail Banking, Energy, and Telecoms. Within these domains, the business partners with industry-leading blue-chip organisations while also remaining well connected to academia and retaining a focus on R&D. This is an incredibly stimulating environment.


The team are obsessive about delivering value for clients and working in a collaborative, engaged and creative way with colleagues and partner businesses.


This Data Scientist role is suited towards candidates with3-5 years of work experience who have technical skills in ML model development, advanced statistics and commercial acumen.


The Role:

  • As aSenior Data Scientist, you will be a technical specialist, developing and implement ML models that deliver tangible value to clients.
  • You will engage with stakeholders to translate business requirements into analytical solutions using the most appropriate data science techniques.
  • You will engage with stakeholders to translate business requirements into analytical solutions using the most appropriate data science techniques.
  • Act as a thought leader, designing solutions from a theoretical standpoint through to practical execution.
  • The role can be remote within the UK.


The Profile:

  • Broad experience of using a range of predictive modelling and machine learning techniques to tackle business problems across commercial sectors.
  • Ability to translate complex analytical solutions into transparent and actionable business insight.
  • Strong stakeholder engagement skills.
  • Advanced knowledge of statistics and ML techniques (both supervised and unsupervised), knowledge of emerging technologies e.g. Reinforcement Learning is advantageous.
  • Advanced user of Python and/or R, with cloud analytics experience.


This is a fantastic opportunity for a passionate experienced data scientist with ambition to grow their career. To apply and grow their analytics skills in multi-disciplinary project teams and collaborate in a fast-growing data science community.


Visa sponsorship is not provided with this role.

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.