Chief AI Officer

Forsyth Barnes
1 year ago
Applications closed

Related Jobs

View all jobs

Automation and Data Engineer

Senior Automation and Data Engineer

Senior Automation and Data Engineer

Head of Data Science

Machine Learning Engineer

Applied AI ML Director - NLP / LLM and Graphs

Job Title: Interim Chief AI Officer

Location: Remote with occasional travel to South of France.

Job Type: Contract

Contract Length:3-6 Months

Contract Type:Outside IR35


The Role:

As Chief AI Officer, reporting to the CEO, you'll lead our global R&D efforts. Your mission is to drive innovation and execution and ensure our AI is state-of-the-art. You will work closely with our data science, development, product, and customer-facing teams to design and implement cutting-edge solutions that deliver real business value.

This role requires a deep understanding of AI, machine learning, knowledge graph, and predictive analytics, coupled with the ability to lead a team of data scientists and software engineers in multiple R&D centers. You must excel in designing, building, optimizing, and utilizing Knowledge Graphs and their ontologies, as these are essential for addressing complex, open-ended challenges in private market investment.


Key Responsibilities:

  • Technical Leadership: Define and drive the technology roadmap, ensuring scalability, reliability, and security of the platform. Oversee AI integration with financial products to meet business goals.
  • Research & Development: Lead AI R&D efforts, working closely with the Chief Scientist to innovate and integrate the latest advancements in machine learning, knowledge graphs, and predictive analytics into fintech solutions.
  • Team Management: Build and lead a high-performing team of engineers, data scientists, and AI/ML specialists across multiple R&D centers, fostering a culture of innovation and excellence.
  • Product Development: Spearhead the design, development, and deployment of AI-based fintech solutions, collaborating with product and customer-facing teams to drive value.
  • Architecture & Infrastructure: Develop scalable technical architecture, optimizing and leveraging semantic web technologies, machine learning with graph data, and cloud-based systems such as Azure and Databricks.
  • Collaboration: Work closely with the CEO, Chief Scientist, and other executive leadership to align technology with business goals. Participate in technical communications with external stakeholders, including universities and research institutions.
  • Risk & Compliance: Ensure that the company's technology infrastructure adheres to regulatory requirements and cybersecurity best practices.
  • Innovation & Strategy: Stay updated on emerging trends in AI and fintech, leading R&D initiatives to maintain a competitive edge in the market.


Key Requirements:

  • Advanced degree, preferably PhD in a related field (e.g., AI, machine learning, data science, statistics, probability), from a prestigious university.
  • 10+ years of experience in AI, machine learning, and predictive analytics.
  • Outstanding people leadership skills, with the ability to mentor and guide a team of data scientist and engineers.
  • Prior experience in strategy consulting (highly desirable).
  • Complex PMO (Project Management Office) experience, including managing multiple large-scale projects simultaneously.
  • Ontology engineering skills: design, develop, and manage ontologies to organize and structure data, ensuring effective data integration, interoperability, and reuse.
  • Semantic Web technologies skills: implement technologies like RDF, OWL, and SPARQL to enhance data connectivity, utilizing semantic web standards for data sharing and representation.
  • Machine learning with graph data skills: utilize machine learning techniques on graph-structured data, enhancing predictive models and analytical capabilities through graph-based learning.
  • Expertise in programming languages and library commonly used in AI and data science, e.g., Python or R, Tensorflow or Pytorch, Darts.
  • Working experience with large-scale data processing frameworks (e.g., Hadoop, Spark, MLFlow …).
  • Working experience with large-scale search engines and DBMS (e.g., Elasticsearch, SQL, Neo4j, CosmosDB).
  • Experience in Training and Monitoring AI, machine learning, and predictive analytics models on Azure and/or Databricks
  • Experience in deployments of AI, machine learning, and predictive analytics production model on Azure and / or Databricks.
  • Ability to take responsibility, initiative, and use excellent comprehension skills to develop solutions to complex AI, machine learning, and predictive analytics problems quickly.
  • Excellent written and verbal English communication skills.
  • A strong bias for decisiveness, action, fail-fast, and the ability to work in a high-velocity, dynamic environment.

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.

Education

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.

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.

🍪 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.