Data Analyst

Morson Talent
Crawley
1 month ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Position Title:

Data Analyst

Contract Duration:

12 months

Location:

Three days per week on site, two days remote working

Overview:

My client is seeking a skilled Data Analyst to support a new project. The role will involve utilising AI and data analysis tools to improve service efficiency, compliance, customer satisfaction, and operational processes by leveraging rich text data. The successful candidate will play a vital role in enhancing business processes and maximising incentive revenue.

Key Responsibilities:

  • Analyse large datasets using AI to extract insights and improve business processes.
  • Develop AI models to categorise free text data across key business areas such as customer comments, job notes, and fault resolutions.
  • Collaborate with business stakeholders to validate use cases, document requirements, and ensure delivery of solutions.
  • Automate review and classification of small service jobs to improve compliance and quality assurance.
  • Conduct sentiment analysis on customer comments to identify positive and negative experiences and improve customer satisfaction.
  • Use AI to optimise fault resolution processes and enhance operational efficiency.
  • Ensure regulatory compliance by validating documentation for planned shutdowns.

Key Competencies:

  • Data Translation Skills: Ability to translate business problems into actionable data models and insights.
  • Strategic Decision-Making: Ability to drive data-led decisions balancing short-term and long-term business needs.
  • Influential Communication: Capable of explaining technical concepts to both technical and non-technical audiences.
  • Organisational Navigation: Skilled in building consensus and managing competing priorities.
  • Technology Agility: Ability to adapt to emerging tools and technologies quickly.
  • Change Catalyst: Identifying impactful changes and making evidence-based recommendations.

Deliverables:

  • Development and deployment of AI models for data categorisation and sentiment analysis.
  • Optimisation of fault resolution processes.
  • Automation of small services job reviews.
  • Documentation and reporting of project milestones and outcomes.
  • Backlog analysis and prioritisation of future use cases.

Essential Skills & Experience:

  • Strong experience in data analysis using Python, SQL, and PowerBI.
  • Experience with data modelling, warehousing, and ETL processes.
  • Excellent communication skills with the ability to engage both technical and non-technical stakeholders.
  • Knowledge of large language models (LLMs) for analysing textual data is desirable.
  • Familiarity with prompt-based AI techniques, context chunking, and few-shot learning is advantageous.

#J-18808-Ljbffr

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.