Senior Project Manager

Ipswich
11 months ago
Applications closed

Related Jobs

View all jobs

Master Data Analyst

Master Data Analyst

Senior Data Engineer: Power BI, Power Apps & ETL

Senior Data Engineer

Senior Cost Intelligence Data Analyst

Senior Cost Intelligence Data Analyst

One of our Civil Engineering clients require a Programme Manager to join their Ipswich based team. The Programme Manager will report directly to the Efficiency & Performance Manager and as a member of the performance team will be responsible for the ‘start to finish’ coordination, management and responsibility of a diverse portfolio of projects within the Client’s Annual Plan.

Key Accountabilities

  • Overall responsibility for the Annual Plan leading on the creation, implementation, monitoring and overall success of the programme and associated KPIs

  • Strong commercial skills including ability to produce commercial reports, accurately forecast and support the business commercial processes

  • Lead on the consistent application and implementation of project management processes and systems across Suffolk Highways

  • Working closely with the Design Manager, Construction Manager and Commercial Manager to monitor and continuously improve the contract performance and associated project delivery KPIs

  • Directly link to the planner roles within the contract ensuring that processes are adhered to and all programmes are accurate and kept up to date

  • Responsible for implementation of project controls and management on projects

  • Co-ordinate cross contract and external resources to deliver key project gateways

  • Work closely with Client leads, cross contract resources, supply chain and other stakeholders to identify and manage project risks, programme, and finances across the project lifecycle

  • Line management responsibility for Project Managers and Administrators

  • Coordinate flow of project information between project teams, Client, and supply chain

  • Develop and apply Power BI to the project management process to improve visibility and effectiveness of reporting and delivery status

  • Take a lead role on raising the commercial awareness across the project management team with a focus on consistent application of change control and financial monitoring

  • Establish close working relationships with client leads across all work streams including cyclical maintenance and planned works

    Skills & Knowledge Requirements:

  • Experience with using project planning software such as Asta PowerProject, Primavera or similar

  • Excellent leadership skills and the ability to demonstrate influencing others in all situations

  • Great team working skills and the ability to collaborate well with others including working in the spirit of mutual trust and co-operation fostering a one team approach to deliver.

  • Excellent communication skills – verbal and written - to ensure clarity in all situations and to effectively influence client decision making.

  • Understanding of the Traffic Management Act (Streetworks Permitting) preferably

  • Experience of using PowerBI reports and dashboards- development of such reports and dashboards will be supped by Data Analysts within the team

    Benefits Include:

    28 days annual leave plus bank holidays

    Generous pension scheme & Life assurance

    Company car or car allowance plus fuel card / Salary sacrifice car finance scheme / Green travel allowance

    Holiday purchase scheme / Referral scheme / Cycle to work scheme

    Employee rewards & discounts platform (includes major retailers & cinemas)

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.