Data Scientist

• Predictive Modeling & Deployment: Design, train, and maintain scalable machine learning and statistical models to tackle complex financial challenges, including predictive credit scoring, risk assessment, and lifetime value modeling.
• Anomaly Detection & Fraud Stewardship: Build and optimize algorithmic security layers to uncover transactional anomalies, flag fraudulent market activity, and ensure platform-wide data integrity.
• Advanced Pipeline Optimization: Partner closely with Data Engineers to scale, maintain, and document underlying feature stores and automated ETL data pipelines, translating architectural constraints into algorithmic efficiencies.
• Cross-Functional Strategy: Bridge the gap between quantitative metrics and financial products by delivering concise algorithmic insights to Product Managers, Risk Officers, and executive stakeholders to drive commercial decision-making.

• Education & Domain Expertise: Bachelor’s or Master’s degree in a highly quantitative field (Data Science, Statistics, Quantitative Finance, Mathematics, or Computer Science) alongside 3 to 5 years of professional experience engineering and deploying production-level predictive models within fintech or corporate banking.
• Machine Learning & Statistical Mastery: Deep technical skill in supervised/unsupervised learning, time-series forecasting, and classification algorithms. Hands-on proficiency with machine learning libraries and deep learning frameworks.
• Data Manipulation & Advanced Querying: Production-grade Python programming skills (highly proficient in Pandas, NumPy) for complex feature engineering, paired with expert-level SQL capability (mastery of window functions, CTEs, query optimization, and performance tuning).
• Big Data & Cloud Infrastructure: Practical experience handling massive, concurrent transactional datasets using distributed computing frameworks (Spark, Hadoop) and modern cloud data warehouses (Snowflake, AWS data architecture).
• Model Rigor & Code Validation: Methodical skill in model validation, back testing methodologies, and identifying/reducing statistical bias, combined with a strong track record of participating in rigorous, collaborative code reviews.
• Global Collaboration & Languages: Bilingual proficiency in English and either Korean, Mandarin, or Japanese is highly preferred and will be heavily utilized given our deeply integrated global client portfolio.

Our client is a premier, institutional-grade financial technology and digital infrastructure enterprise (1,000+ employees) modernizing high-scale quantitative modeling, automated risk compliance, and enterprise AI-driven market intelligence solutions. We are seeking an experienced, highly quantitative Data Scientist to drive the design, development, and optimization of their core financial intelligence engines. This mid-level role is a high-impact career accelerator, focusing on deploying complex predictive models, refining risk and anomaly tracking algorithms, and leading cross-functional data discovery.

Why Join Us

• Quantitative Leadership Track: Benefit from clear avenues to high-impact model architecture ownership and ongoing strategic development coaching from senior quantitative scientists and finance veterans.
• Massive Financial Datasets: Take autonomous ownership of enterprise-scale feature engineering, building and validating models on millions of concurrent transactional data points across global markets.
• Modern High-Performance Stack: Work daily with premier enterprise data configurations and high-performance cloud data ecosystems (Python, SQL, PyTorch/Scikit-Learn, Snowflake, Spark, and AWS).