Business Process Analyst. Strong process mapping, analysis and Communication skills

Great Lea Common
2 months ago
Applications closed

Related Jobs

View all jobs

Reporting and Insights Manager

Data Checking Administrator

Business Data Analyst

Data Analyst

Data Scientist

Network Analyst

Business Process Analyst / Business Data Analyst / Process Mapping

The Business Process Analyst will support the Product and Technical team by assisting in business process analysis and requirements definition specifically within the Automotive Aftersales industry. 

The role involves gathering, analysing, and interpreting business requirements alongside business processes from Vehicle Manufacturers and Automotive Dealerships.

The ideal candidate will have strong analytical skills, a keen interest in the automotive industry, and the ability to work collaboratively in a fast-paced environment.

Key Responsibilities

Business Process Analysis:

• Assist in documenting and analysing current business processes, identifying areas for improvement

• Collaborate with various departments to gather requirements and suggest enhancements.

• Work with cross functional teams to drive continuous improvement to drive efficiency and quality Project Support:

• Participate in project meetings, providing analytical support and assisting in project documentation

• Assist in the implementation of new systems, tools, or processes

• Conduct thorough User Acceptance Tests to ensure Product changes are delivered as per business requirements 

• Provide documentation and operational support post implementation of changes Data Analysis & Reporting:  

• Collect and analyse data related to sales, market trends, customer behaviour, and product performance

• Assist in the preparation of detailed reports, dashboards, and presentations to support business decisions

• Monitor and track key performance indicators (KPIs) to evaluate the effectiveness of strategies and initiatives Communication & Collaboration:

• Work closely with Product teams, Development Teams, Managers, and cross-functional teams to understand business needs

• Communicate findings and insights clearly and effectively to stakeholders Skills & Competencies:  

• Strong analytical and problem-solving skills

• Good understanding of data visualization techniques and tools

• Basic understanding of Agile/Scrum methodologies

• Proficiency in Microsoft Excel, PowerPoint, and other data analysis tools

• Excellent communication and interpersonal skills

• Ability to work both independently and collaboratively in a team environment

• Detail-oriented with strong organizational skills

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.

Jobs

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!

Jobs Careers

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.

Jobs

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.

🍪 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.