Data Analyst - Operations/Production

Marshall Group
Cambridge
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Your responsibilities in this role include:

Working closely with the Delivery Performance Manager, this role will support the delivery of our strategy within our Deployable Infrastructure business lines. Manage data integrity and drive change as appropriate to meet the needs of the business.

  • Consult with Delivery Performance Manager to identify improvement opportunities for transformation within the business unit.
  • Contribute to promoting the adoption of our digital tools and systems
  • Lead efforts to improve data integrity and accuracy within our ERP systems
  • Leverage technology to optimise efficiencies within our current processes
  • Contribute to the development of the appropriate technical and behavioural skills to enable a culture of continuous sustained with the Delivery Performance Manager and DI&VMS teams in improvement in achieving low-cost, high-quality operations
  • Communicating across the business to the relevant stakeholders
  • Manage and control data integrity of production capacity, resource supply and demand schedule for active and prospective opportunities
  • Enhance data processing and collation methods, ensuring existing practices and data collation methods are optimised
  • Support with establishing and agree the priorities of the improvement projects using a defined and agreed set of criteria
  • Collaborate with the Delivery Performance Manager to support with implementation and maintenance of the Business improvement plan to enable agreement of project priorities
  • Extract raw data and transform to generate meaningful insights in support of decision making
  • Build, develop and maintain visual dashboards / KPI's using PowerBi & Excel to support the interpretation of the information and communication of the insights
  • Support the function stakeholders within the DI&VMS business lines to create smarter and well-rounded solutions
  • Generation of reports both written and data based

Apply if you have most of the following:

  • Proven experience as a Data Analyst within a Production/Manufacturing environment
  • Understanding and familiarisation of using ERP
  • Demonstrable Intermediate Microsoft Excel user
  • Working skills in PowerBi or other dashboarding and analysis tool
  • Data Management and interpretation within a business setting
  • Good presentation skills
  • Able to view issues from an analytical perspective with an ability to deconstruct processes
  • Ability to influence and negotiate with key stakeholders at all levels
  • Able to resolve conflict and collaborate across multidisciplinary teams
  • Resilience in change
  • Attention to detail
  • Prioritise and solve problems, take a pragmatic approach to tackling issues within and across functions


The benefits we will offer you include:

  • 27 days holiday increasing with service up to 30 days (option to buy /sell)
  • Pension contributions up to 9%
  • Healthcare cash plan for you and your children
  • Extensive flexible benefit program including Cycle to Work
  • Life assurance at 4x basic salary
  • Enhanced parental leave and pay
  • Paid volunteering leave
  • Access to industry leading wellbeing resources and tools
  • Opportunities to develop your skill set and gain additional qualifications

#LI-DS1

#LI-Hybrid

IND01

RC5TZXdlbGwuMzgyMTAuMTIyNzFAbWFyc2hhbGwuYXBsaXRyYWsuY29t.gif

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.