Head of Data and AI

Introduction

Currenxie is a financial technology company for borderless businesses. Founded in 2014, our Global Account platform offers businesses of all sizes a single finance app for global growth. Features include multi-currency digital wallets, on-demand virtual bank accounts, worldwide payments, currency exchange, and instant Visa cards. We value innovative technology, user experience, and service.

We are an ambitious and rapidly growing company led by an entrepreneurial and collaborative team. We offer our people an opportunity to be part of building a new financial services platform for a borderless world, and to learn and progress in a friendly, dynamic, and multi-cultural environment.

Job Description

We are looking for an experienced and entrepreneurial Head of Data and AI to lead our data strategy, engineering, advanced analytics, and artificial intelligence initiatives. In this role, you will define and execute the vision for how Currenxie leverages data and AI to optimize our core financial products, automate critical workflows, mitigate risk, and unlock new business capabilities.

You will act as both a strategic leader and a hands-on technical builder, growing and mentoring a high-performing team of data engineers, data scientists, and analysts. We’d like that you aren’t afraid of voicing an opinion on how to do things better—from platform architecture and data governance to introducing cutting-edge AI and LLM-driven tooling. You must be a driven team player who balances fast execution with high standards for data integrity and security, comfortably driving transformation in a dynamic, multinational fintech environment.

Responsibilities

• Define, execute, and own Currenxie’s enterprise Data and AI roadmap, aligning data initiatives with product innovation, compliance, and commercial growth.
• Architect, scale, and maintain a modern, reliable, and secure data platform supporting both real-time operational needs and analytical reporting.
• Lead the discovery, development, and production deployment of machine learning and AI capabilities (including predictive models, transaction monitoring, and Generative AI/LLM workflows).
• Build, mentor, and manage a multidisciplinary team spanning data engineering, data science, and business intelligence.
• Partner with engineering, product, compliance, and executive stakeholders to identify high-impact use cases and turn data into actionable product features and business insights.
• Establish robust data governance, data quality, privacy, and Responsible AI frameworks to satisfy stringent fintech security and regulatory standards.
• Champion a company-wide culture of data literacy and experimentation, establishing best practices for MLOps, CI/CD for data, and automated testing.

Skills

• 8+ years of total experience across data engineering, data science, and analytics, with 3+ years in a senior leadership or management capacity.
• Proven track record of designing, deploying, and maintaining production-grade data platforms and machine learning systems at scale.
• Strong technical background in modern data architectures (e.g., Snowflake, BigQuery, Databricks, PostgreSQL, dbt, Apache Airflow/Prefect, Kafka).
• Hands-on proficiency with Python, SQL, and modern machine learning frameworks, with a clear understanding of production MLOps practices.
• Practical understanding of modern Generative AI, LLMs, prompt engineering, and agentic workflows, focusing on real-world business ROI rather than market hype.
• Solid grasp of data governance, data modeling, role-based access control, and privacy standards (e.g., GDPR, PCI-DSS).
• Degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related quantitative field.
• Exceptional communication skills in English, with the ability to translate complex data and AI concepts into clear business strategy for non-technical stakeholders.
• A player-coach mindset: comfortable contributing to architecture and technical decisions while mentoring the team and steering executive strategy.

Bonus

• Domain experience in fintech, e-wallets, global payments, FX, cards, or banking.
• Experience deploying data and AI solutions for transaction fraud detection, AML/KYC monitoring, or automated underwriting.
• Strong hands-on expertise with AWS cloud infrastructure, security, and containerized deployments (Docker, Kubernetes).
• Experience with building LLM-assisted internal tooling, customer-support automation, or autonomous workflow agents.

Data collected will be treated in strict confidence and used for recruitment purposes only. Currenxie does not accept unsolicited resumes from search firm recruiters. Fees will not be paid in the event a candidate submitted by a recruiter without an agreement in place is hired; such resumes will be deemed the sole property of Currenxie.