Technology Advisor

Leap29
London
1 year ago
Applications closed

Related Jobs

View all jobs

Advisory Data Engineer

Data Engineer

Senior Consultant - AI & Data, Financial Services, Data Platforms, Data Engineer, BCM, Edinburgh

Senior Consultant, Data Engineer, AI&Data, UKI, London

Revenue Assurance Data Analyst (Maternity Cover)

Senior Consultant - AI & Data, Financial Services, Data Platforms, Data Engineer, BCM, Edinburgh

body {font: normal 400 13px/16px 'Open Sans','Helvetica Neue',Helvetica,Arial,sans-serif;color:#333;margin:10px;}p,span,div,b,i,u{ line-height: 120% !important; font-family: 'Open Sans','Helvetica Neue',Helvetica,Arial,sans-serif !important; }

Technology Advisor is required for a non-profit organisation company, working with political leaders to drive change. They want you to become an integral part of their dynamic team in Ethiopia as a Capital Markets Advisor on Technology and Skills Development. The ideal candidate will bring a wealth of knowledge spanning across technology and experience in advisory on government digital transformation. Working closely with the Director General of the Ethiopian Capital Markets Authority (ECMA), the Advisor will provide strategy and technical support to ensure the implementation of a coherent and impact-driven digital transformation roadmap and implement capacity building initiatives on digital literacy and skills for ECMA to promote the integration of tech-enabled solutions and utilisation of digital technologies.

Details:

Visa and relocation will be supported by the company.

Key Responsibilities

Strategy development

Collaborate with the ECMA team to conduct a gap assessment of the ECMA technology landscape Provide strategic guidance in developing and implementing a comprehensive 5-Year Digital Transformation Roadmap to advance the modernisation and growth of Ethiopia’s capital markets Support research and development on emerging technologies (including blockchain), platforms and regulatory frameworks to inform ECMA’s technology adoption Support change management initiatives to ensure the smooth adoption of new technologies and processes Promote a culture of innovation within ECMA that supports exploratory digital and technology projects and proofs of concept using emerging technologies

Person Specification

Experience

A minimum of 10 years’ experience in management consulting or capital markets advisory related to technology and digital transformationAn understanding of capital markets products and operations, including business and systems perspectives, as well as regulatory and business knowledgeProven experience evaluating and implementing emerging technologies in capital markets such as digital trading, investment, and market surveillance platformsWorking knowledge and experience of Big Data AnalyticsPrior experience working on digitisation programmes/projects in financial markets and institutions (such as capital market regulators, investment banks or other market participants) is highly desirableProven understanding of the policy context, how to shape policy and the discretion and judgment needed to navigate a complex political environmentExperience in planning and conducting business and technical stakeholder interviews, undertaking requirements analysis, performing current state technology analysis, and developing digital strategiesPrior experience working on digitisation programmes/projects in emerging markets, especially in Sub-Saharan Africa, is highly desirable

Skills and Competencies

Strong grasp of financial technology (Fintech) trends and toolsProven ability to leverage statistical analysis and modelling techniques to solve complex problems and extract actionable insights from complex financial data with added skills in data visualisationAbility to stay current on regulatory developments in the financial technology and digital spaceUnderstanding of capital markets products such as equities, bonds, derivatives, and other financial instruments is desirableExcellent verbal and writing skills with the ability to articulate technical concepts to diverse audiencesExcellent project management skills, including prioritising tasks, managing resources, and mitigating risks effectivelyAbility to work flexibly and be adaptable and open to changing approaches in response to evolving situationsAbility to work collaboratively in a team-oriented environment and independently with minimal supervisionMust be fluent in English. Working knowledge of Amharic is an added advantage

Qualifications

Master’s Degree in Computer Science, Information Technology, or related fieldRelevant certifications in the field of Information TechnologyRelevant capital markets certifications or qualifications are desirable

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.