Finance Data Science Analyst

As part of SGX’s Financial Technology (Fintech) team, the Financial Data Scientist applies machine learning on financial and business data to build predictive models, uncover trends, and support decision-making processes. This role blends data science and financial expertise to enhance forecasting, identify risks, and deliver insights through collaboration with finance, technology, and business unit counterparts.

Key Responsibilities:

• Collect, cleanse, and prepare financial and operational data from internal and external sources, ensuring data integrity and security.
• Perform data analysis and modeling, develop codes and algorithms to improve predictive models, and identify trends and anomalies.
• Automate and streamline financial reporting processes, including dashboards and ad-hoc analysis, to improve productivity and support decision-making.
• Communicate insights and recommendations through clear visualizations and reports tailored to the stakeholders
• Collaborate with cross-functional teams to identify business challenges and provide data-driven recommendations.

Qualifications and Requirements:

Good degree in data science or a related field, such as:

• Data Analytics, Computer Science, or Mathematics.
• Finance, Accountancy, Economics.

Essential Skills:

• Analytical and problem-solving skills: skilled in analyzing complex data, learning patterns, generating meaningful insights, and developing data-driven solutions
• Communication skills: good command in English (verbal and written), able to effectively communicate technical information to technical and non-technical stakeholders.
• Technical proficiency: Python and SQL programming, data warehousing (Snowflake), AI and machine learning (LLM), and proficiency with visualization tools (Tableau, Power BI).
• Financial acumen: understanding of financial concepts, forecasting and financial modelling.

Preferred Attributes:

• Self-starter with a growth mindset and strong ownership of tasks and outcomes.
• Able to work independently and collaboratively in a data-driven environment.
• Passionate in using data-driven approaches to solve business problems.