Grants Data Analyst and Administrator

City of London
1 year ago
Applications closed

Related Jobs

View all jobs

Funds Technology – Data Analyst Manager Assistant Manager Senior Consultant

Freelance: Online Data Analyst - Latvian speaker (UK)

Research Assistant/Associate in Exoplanetary Remote Sensing and Data Science (up to 2 posts) (F[...]

Data Scientist - Life - 12 month FTC

Data Scientist - Life - 12 month FTC

Data Scientist - Life - 12 month FTC

Grants Data Analyst and Administrator

We are working with a property and investment based charity in central London that is seeking an experienced data administrator to support their philanthropy team processing, analysing and reporting on their Grant activity. This is a diverse role where you will be involved with data analytics and reporting on the impact that grants have made. Traditionally their grants / donations are focussed around housing, the elderly, education, churches and community and you’ll be key to ensuring they have maximum positive impact.

On a day-to-day basis you’ll be involved with:

  • Grant data management through the full grant cycle

  • Logging and processing grant applications

  • Reviewing banking information

  • Preparing data and reports for committee meetings, presentations and internal stakeholders

  • Reporting on the impact grants have made to grantees

  • Assisting with the logistics and administration around events

  • Ensuring the charity’s website content is up to date

  • Providing administrative and logistical support to the Operations and Impact Manager and wider team

    The ideal candidate will have worked in a similar role, have worked in the not for profit sector and strong data analysis and administration skills. It’s essential that you have advanced MS Office skills, CRM experience, along excellent organisational skills and the ability to work to deadlines.

    This is a permanent role offering a salary up to £35,000 per annum, dependent on skills and experience. With this there is a very competitive benefits scheme that includes a generous holiday allowance (30 days per annum) and a free lunch every day. Please note you will be required in the office full-time, which is within walking distance of Liverpool Street, Moorgate, Bank and Cannon Street stations.

    If the role is of interest please submit your CV ASAP. Please note that we consider every CV submitted to us, however due to the high volume of applications and time constraints we are only able to get back to those applications that are successful

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.

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.

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.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.