Managing Consultant - Digital Analytics & AI

Graduate Recruitment Bureau
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Consultant_ Data Analyst

Senior Consultant_ Data Analyst

Senior Consultant - Data Scientist

Senior Consultant - Data Scientist

Principal Consultant - Data Engineering Lead

Consultant - Manager, Data Engineer, AI & Data, Defence & Security

About the Company

Specialising in digital innovation, design, and transformation, our client helps CxOs envision and shape the future of their businesses. Their team of over 10,000 professionals, including strategists, data scientists, product designers, experience designers, brand experts, and technologists, develops new digital services, products, experiences, and business models for sustainable growth.

As a global leader, they work with companies to transform and manage their businesses through advanced technology. Dedicated to unlocking maximum potential via technology for a more inclusive and sustainable future, they prioritise responsible operations and diversity across more than 50 countries. With a rich 50-year history and deep industry knowledge, they are trusted to address all aspects of business needs, from strategy and design to operations, driven by the rapidly evolving fields of cloud computing, data analytics, AI, connectivity, software, digital engineering, and platforms.

The Team

As a Customer and Digital Analytics & AI Managing Consultant/Senior Manager, the successful candidate will become a pivotal member of the team. They will have the opportunity to work across various sectors and industries, with a specific focus on Customer and Digital Analytics. Collaborating with market-leading clients, they will apply cutting-edge technologies to solve impactful commercial growth challenges.

In this role, they will lead and manage complex analytics projects, provide strategic insights to clients, and drive the growth of the Analytics and AI consulting practice. Collaborating with cross-functional teams, they will leverage their expertise to deliver impactful solutions that enhance customer experience, optimise marketing efforts, and drive business growth. Their skill in building connections will drive sales opportunities, contributing to overall company success. Embracing Agile project management, they will demonstrate dynamic leadership, navigating complex business landscapes through business analysis, process modelling, requirements analysis, and use case modelling.

Key Responsibilities

Lead and Manage Projects: Oversee multiple analytics projects, ensuring timely delivery and high-quality outputs. Develop project plans, allocate resources, and oversee project execution.Client Relationships: Build and maintain strong relationships with clients, understand their business needs, and provide tailored analytics solutions. Present findings and recommendations to senior stakeholders.Team Mentorship: Mentor and guide junior team members, fostering a collaborative and high-performance culture. Provide technical and analytical support to the team.Technical Proficiency: Demonstrate proficiency in analytical tools and programming languages such as Python, R, and SQL, along with experience in data visualisation tools like Tableau or Power BI. Knowledge of AI and machine learning techniques, including generative AI, is essential.Problem-Solving: Exhibit strong problem-solving skills and the ability to analyse complex data sets. Experience with statistical modelling, predictive analytics, and data mining is required.

This role offers a unique opportunity to work at the forefront of digital analytics and AI, making a significant impact on client success and driving innovative business growth.

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.

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.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.