Head of Data Science

Fingular is a global independent fintech holding headquartered in Singapore, established in October 2021. The company manages a diverse portfolio of fintech products, operating under its own brand or in collaboration with trusted local partners. Fingular currently operates in multiple markets, including Indonesia, Malaysia, India. The company offers fully digital financial products — including consumer financing, investment and savings products — tailored to meet local needs and cultural specifics.

We are seeking an experienced
Head of Data Science
to lead our Data Science and Computer Vision teams and drive the development of advanced machine learning solutions across the organization. This role requires a strong technical foundation in machine learning, deep learning, and modern AI/LLM methodologies, combined with excellent leadership and communication skills. The ideal candidate is both a strategic thinker and a hands-on practitioner, capable of developing and guiding the full lifecycle of model development—from ideation and research to deployment and long-term maintenance.

Leadership & Strategy

• Lead, mentor, and develop a multidisciplinary team of Data Scientists, Machine Learning Engineers, and Computer Vision Engineers.
• Define the Data Science strategy and roadmap aligned with company goals.
• Foster a culture of innovation, experimentation, and scientific rigor.
• Collaborate closely with product, engineering, operations, and business stakeholders to translate business objectives into actionable AI/ML solutions.

Technical Execution

• Oversee end-to-end development of machine learning models, from data exploration and feature engineering to production deployment.
• Build and maintain efficient, scalable ML pipelines and workflows.
• Ensure high standards in model quality, validation, reproducibility, and monitoring.
• Guide the development, deployment, and optimization of models for both tabular and computer vision tasks.
• Architect and maintain ML infrastructure, including tools such as MLflow, model registries, CI/CD for ML, and monitoring systems.

AI & LLM Development

• Stay up to date with state-of-the-art AI and LLM techniques, including fine-tuning, retrieval-augmented generation (RAG), and parameter-efficient methods (e.g., LoRA).
• Evaluate and integrate modern LLM frameworks and APIs, such as Qwen, OpenAI, and open-source alternatives.
• Lead experimentation with LLM-based features and pipelines.

Cross-Functional Collaboration

• Communicate complex technical concepts to non-technical stakeholders in a clear and actionable way.
• Work with data engineering to ensure high-quality, reliable data pipelines.
• Align data science initiatives with product roadmaps and business KPIs.

Operational Excellence:

• Oversee ongoing monitoring, maintenance, and performance tuning of deployed ML services.
• Establish and enforce best practices for versioning, reproducibility, testing, and documentation.
• Ensure data governance, privacy, and compliance with relevant regulations.

Required Skills & Qualifications:

• Proven experience in a senior or lead Data Science role; prior experience managing a team is essential.
• Deep understanding of machine learning, deep learning, and statistical modeling.
• Strong hands-on experience with model development and deployment in real-world production environments.
• Expertise with classical ML techniques (e.g., Logistic Regression, CART, Gradient Boosting) and modern deep learning architectures.
• Solid understanding of ML operations and infrastructure (e.g., MLflow, model registries, pipeline orchestration, monitoring).
• Experience with LLMs, including fine-tuning and integrating state-of-the-art models (e.g., LoRA, Qwen, OpenAI models).
• Strong programming skills in Python, and familiarity with Typescript for integrations or tooling.
• Proficiency in working with databases such as ClickHouse and PostgreSQL.
• Experience with cloud services (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
• Strong professional English communication skills (written and verbal) are mandatory due to cross-department collaboration and documentation requirements.
• Excellent communication, leadership, and stakeholder management skills.
• Ability to drive projects independently and manage multiple priorities in a fast-paced environment.

Preferred Qualifications:

• Experience working with computer vision models and frameworks.
• Familiarity with Typescript and React.
• Familiarity with distributed computing frameworks (py-spark).
• Understanding of data engineering fundamentals and ETL/ELT pipelines.
• Prior involvement in building or scaling ML platforms or internal AI tooling.
• Background in experimentation, A/B testing, and causal inference methods.