Head of Machine Learning, Ongoing Monitoring
TideABOUT TIDE
At Tide, we help SMEs save time and money in the running of their businesses by not only offering business accounts and related banking services, but also a comprehensive set of highly usable and connected administrative solutions, from invoicing to accounting.
Tide is transforming the small business banking market and now supports over 2 million members globally across the UK, India, Germany and France.
Using advanced technology, all solutions are designed with SMEs in mind. With quick onboarding, low fees and innovative features, we thrive on making data driven decisions to serve our mission: to help SMEs save time and money so they can get back to doing what they love.
Tide facts:
- Tide is available for UK, Indian, German and French SMEs
- Over 2 million members: 900,000 UK and 1,100,000 in India and growing rapidly
- Over $300 million raised in funding
- Over 2,800 Tideans globally
- Recognised with Great Place to Work certification three years in a row, and among India’s Top 50 Best Workplaces in Banking, Financial Services, and Insurance in 2026
- We have offices in Central London, with a member support and technology centre in Sofia, Bulgaria, technology centres in Serbia, Romania, Lithuania and Hyderabad and offices in Gurugram, New Delhi, Berlin, Paris and Luxembourg
ABOUT THE TEAM:
The Ongoing Monitoring team sits at the core of Tide’s trust and safety ecosystem, building the systems that power our fraud detection, financial risk, and regulatory compliance capabilities. As Tide continues to scale, the team is responsible for designing and evolving intelligent, resilient, and adaptive platforms that protect our members and keep the business safe.
Working closely with Product, Data Science, Design, Compliance, and Operations, the team combines deep technical expertise with a strong focus on member experience, ensuring that our risk and monitoring capabilities are both effective and intuitive. This is a high-impact area at a pivotal stage of Tide’s growth, offering the opportunity to shape the future of AI-driven fraud and risk management at scale.
ABOUT THE ROLE:
As Head of Machine Learning you will:
- Define and execute a risk engineering strategy: develop plans that align with Tide’s long-term vision to ensure that our approach to risk supports rapid and compliant growth.
- Lead a team of talented managers and engineers: foster a culture of excellence, innovation, user-centric design, and collaboration in our engineering team.
- Build and scale risk infrastructure: architect and scale foundational risk systems to support real-time decision-making, effective data management, and advanced fraud prevention.
- Develop self-service tools for internal teams: empower product, compliance, and risk operations teams with extensive and user-friendly risk tooling, enabling independent monitoring and management of risk processes and reducing dependencies for day-to-day operations.
- Support compliance and regulatory standards: collaborate with legal and compliance experts to keep Tide a compliance leader in an evolving regulatory climate.
- Champion data-driven risk management: lead initiatives to leverage data and machine learning in fraud detection and compliance, providing Tide with actionable insights and automated solutions that improve our resilience.
WHAT WE ARE LOOKING FOR:
- Proven leadership in engineering: 12+ years of engineering experience with at least 2 years in a senior leadership role (e.g., head, director, or senior manager), ideally within fintech or regulated technology environments.
- Risk and compliance expertise: deep experience in fraud prevention and compliance (KYC/KYB, AML), ideally within the financial services industry.
- Machine Learning: Strong experience in supervised and unsupervised models, decision trees, neural networks, and Large Language Models (LLMs).
- Agent Frameworks: Hands-on experience with multi-agent orchestration frameworks (e.g., LangGraph, AutoGen) used to build self-correcting compliance workflows.
- Fintec
Similar roles
Design & Tech
Related reads from TCHNX
The Emergence of Small Language Models: Why Efficiency Is Overtaking Scale
As the AI industry confronts computational costs and environmental concerns, a new generation of compact models is proving that bigger isn't always better. Small language models are reshaping enterprise AI deployment.
Algorithmic Bias in Design Systems: Why Your AI-Generated UI Might Exclude Users
As AI tools increasingly generate interface components, they're embedding biases that systematically exclude users. Understanding how machine learning models inherit prejudice is essential for creating truly inclusive design systems.
The Synthetic Design Problem: Can AI-Generated Assets Ever Replace Human Creativity?
As AI design tools flood creative workflows, a fundamental question emerges: are we automating creativity or merely industrialising pastiche? The answer reshapes how we understand design innovation itself.