Head of Data and AI

Currenxie

. Define, execute, and own Currenxie’s enterprise Data and AI roadmap .

Posted 10/27/2026full-timeQuarry Bay • Hong KongLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in defining and executing data and AI strategies, with a strong focus on building and maintaining scalable data platforms and machine learning systems. Proven ability to lead multidisciplinary teams and translate complex data concepts into actionable business insights.

Highest-signal resume keywords

Data Platform ArchitectureMachine Learning Systems DeploymentMLOps Best PracticesData Governance and Privacy StandardsTeam Leadership and Mentorship

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringData ScienceAnalyticsPythonSQLMachine Learning FrameworksData ModelingGenerative AIPrompt EngineeringProduction MLOps

Soft Skills

Exceptional Communication SkillsPlayer-Coach Mindset

Tools & Technologies

SnowflakeBigQueryDatabricksPostgreSQLDbtApache AirflowPrefectKafka

Industry Keywords

Data GovernanceData QualityPrivacy StandardsGDPRPCI-DSS

Tech Stack

Tools & technologies

AirflowApacheBigQueryKafkaPostgresPythonSnowflakeSQL

About the role

Key responsibilities & impact

Define, execute, and own Currenxie’s enterprise Data and AI roadmap
Align data initiatives with product innovation, compliance, and commercial growth
Architect, scale, and maintain a modern, reliable, and secure data platform
Lead 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
Turn data into actionable product features and business insights
Establish data governance, data quality, privacy, and Responsible AI frameworks
Champion company-wide data literacy and experimentation
Establish best practices for MLOps, CI/CD for data, and automated testing

Requirements

What you’ll need

8+ years of total experience across data engineering, data science, and analytics
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
Clear understanding of production MLOps practices
Practical understanding of Generative AI, LLMs, prompt engineering, and agentic workflows
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
Ability to translate complex data and AI concepts into clear business strategy for non-technical stakeholders
Player-coach mindset; comfortable contributing to architecture and technical decisions while mentoring the team and steering executive strategy

Benefits

Comp & perks

Opportunity to be part of building a new financial services platform for a borderless world
Learn and progress in a friendly, dynamic, and multi-cultural environment