Digital Development Manager

Ipswich
10 months ago
Applications closed

Related Jobs

View all jobs

Analytics Specialist with Data Science

Data Engineering Manager

Data Engineering Manager

Machine Learning Engineer (Manager)

Machine Learning Engineer (Manager)

Machine Learning Engineer (Manager)

Software Development Manager - Pioneering Tech Innovation I'm working with a market-leading manufacturer based in Ipswich seeking a Software Development Manager to drive their digital transformation journey. This is a brand-new position created to spearhead innovation across the business.

The Role Reporting to the Head of IT, you'll combine strategic leadership with hands-on development expertise to deliver cutting-edge digital solutions while building a high-performing team.

Key Responsibilities:
Lead the application support team, managing workloads and day-to-day operations
Contribute directly to development tasks, ensuring high-quality technical solutions
Implement and maintain robust development processes and secure lifecycles
Identify opportunities to enhance the digital portfolio through innovation
Oversee Power BI development and administration
Establish comprehensive data and reporting strategies
Develop clean, efficient, maintainable code
Introduce low/no-code solutions to optimize the Microsoft stack
Ensure seamless integration between digital platforms and core systemsWhat My Client Needs
Proven experience in UI design, secure applications, and database management
Understanding of complex RESTful web APIs
Leadership experience in digital development roles
In-depth knowledge of web technologies (HTML5, CSS, HTTP, JavaScript, PHP, .NET)
Extensive Microsoft Power Platform expertise (Apps, BI, Automate, Dataverse, Fabric)
Strong grasp of emerging technologies, AI, and machine learning
Familiarity with agile methodologies
Knowledge of CRM/ERP systems and Microsoft Azure/cloud technologies
Previous team management experience with mentoring abilitiesThe Ideal Candidate
Confident and engaging with a positive, proactive approach
Strong leadership skills with excellent communication abilities
Highly proactive with impressive problem-solving capabilities
Excellent time management and prioritization skills
Ability to navigate IT governance, controls and risk managementPackage Details
Competitive salary based on experience
Annual performance bonus
Comprehensive benefits including life assurance and healthcare
Excellent pension scheme
20 days annual leave (increasing to 25 after 1 year)
Extensive development and wellness programmesWorking Arrangements
Full-time, permanent position
Ipswich, Suffolk location with hybrid working options after probationIf you're a tech leader passionate about driving digital transformation in a collaborative, fast-paced environment, I'd love to discuss this opportunity with you

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.

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

🍪 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.