Junior/Senior / Lead Data Scientist (Financial AI & Risk Analytics)

About the role

We are seeking an experienced Junior/Senior / Lead Data Scientist specialising in Financial AI & Risk Analytics to join our team at our Din Daeng Bangkok office on a full-time basis. In this pivotal role, you will lead the development and implementation of sophisticated artificial intelligence and machine learning models designed to address complex financial risk challenges and deliver actionable insights to our stakeholders. You will have a strategic impact on our analytical capabilities, driving innovation in financial AI whilst establishing best practices across our data science operations. This position offers the opportunity to work at the intersection of finance and technology, where your expertise will directly influence business outcomes and competitive advantage.

Key responsibilities

1. Design, develop and deploy advanced machine learning and artificial intelligence models focused on financial risk analytics, credit risk assessment, market risk analysis and fraud detection
2. Lead the end-to-end data science lifecycle, from problem definition and data exploration through model validation, implementation and ongoing performance monitoring
3. Mentor and supervise junior data scientists and analytical staff, fostering a culture of excellence, continuous learning and collaborative problem-solving within the team
4. Conduct comprehensive exploratory data analysis on large-scale financial datasets to identify patterns, anomalies and opportunities for predictive modelling
5. Develop robust statistical models and econometric frameworks to quantify and forecast financial risks across various asset classes and business segments
6. Create clear and compelling data visualisations and reports that translate complex analytical findings into actionable business recommendations for senior management and risk committees
7. Collaborate closely with risk management, compliance, trading and operations teams to understand business requirements and ensure analytical solutions align with strategic objectives
8. Establish and maintain rigorous model governance frameworks, including validation protocols, backtesting procedures and documentation standards to ensure regulatory compliance
9. Stay abreast of emerging trends, methodologies and technologies in financial AI, machine learning and risk analytics, and evaluate their applicability to our business
10. Optimise data pipelines and analytical infrastructure to enhance processing efficiency, scalability and data quality across analytical systems

What we're looking for

1. Master's degree or higher in Data Science, Machine Learning, Statistics, Mathematics, Physics, Computer Science or a related quantitative discipline
2. Minimum 7–10 years of professional experience in data science, machine learning engineering or advanced analytics, with at least 4–5 years specifically focused on financial services, risk analytics or quantitative finance
3. Demonstrated expertise in building and deploying machine learning models in production environments, with proficiency in supervised learning, unsupervised learning, ensemble methods and deep learning techniques
4. Strong proficiency in programming languages such as Python, R or Scala, with experience in relevant libraries and frameworks (e.g. scikit-learn, TensorFlow, PyTorch, XGBoost)
5. Solid understanding of financial concepts including credit risk, market risk, operational risk, counterparty risk, regulatory capital requirements and risk measurement methodologies
6. Experience with big data technologies and distributed computing frameworks such as Apache Spark, Hadoop or cloud-based analytics platforms (AWS, GCP, Azure)
7. Proven track record of leading analytical projects, mentoring junior staff and driving cross-functional collaboration in a complex organisational environment
8. Strong statistical knowledge including hypothesis testing, causal inference, time series analysis and experimental design
9. Experience with financial data sources, APIs and market data platforms; familiarity with data warehousing and ETL processes is advantageous
10. Excellent communication skills with the ability to present technical concepts clearly to both technical and non-technical audiences
11. Knowledge of financial regulations such as Basel III, IFRS 9, Dodd-Frank or equivalent regional frameworks is highly desirable
12. Certification in machine learning, data science or related disciplines is a plus