DATA SCIENTIST

Job Overview

We are seeking a Data Scientist to develop and enhance data-driven models for a business digital risk intelligence platform covering Asia Pacific and India.

The role focuses on analysing large-scale, longitudinal datasets comprising publicly observable business information, identifying meaningful patterns and changes over time, and translating these signals into reliable risk scores and actionable insights. The successful candidate will contribute to the development, validation and optimisation of machine learning models that support business risk assessment, market intelligence and change detection.

Key Responsibilities

• Develop, evaluate and refine machine learning models to assess business digital risk using publicly observable data.
• Analyse longitudinal datasets to identify changes in business digital footprints, detect anomalies and uncover emerging risk indicators.
• Design and implement features using historical observations, statistical analysis and time-series techniques to improve predictive performance.
• Develop business risk scoring methodologies and validate their reliability, consistency and predictive value.
• Perform data cleaning, feature engineering, entity matching and data quality analysis to improve the accuracy and integrity of business records.
• Develop analytical methods to classify businesses and normalise risk scores across different industries and geographical markets.
• Build and optimise data processing and modelling pipelines using Python, SQL and relevant data science frameworks.
• Evaluate model performance through appropriate statistical methods, validation frameworks and performance monitoring.
• Translate research findings and analytical results into practical scoring models, reports and product features.
• Collaborate with engineering and product stakeholders to integrate data science solutions into the company’s digital risk intelligence platform.

Requirements

• Degree in Data Science, Computer Science, Statistics, Mathematics, Artificial Intelligence or a related quantitative discipline.
• Strong programming skills in Python and experience using SQL for data analysis and processing.
• Practical experience in developing, evaluating and deploying machine learning models.
• Strong understanding of statistical analysis, predictive modelling, feature engineering and model validation.
• Experience analysing large datasets and identifying patterns, trends and anomalies.
• Strong analytical, quantitative reasoning and problem-solving skills.
• Ability to translate complex data into reliable models and actionable business insights.

Preferred Experience

• Experience with time-series analysis, longitudinal datasets, anomaly detection or change detection.
• Experience developing risk scoring models, classification systems or predictive analytics solutions.
• Familiarity with data science frameworks such as scikit-learn, PyTorch or TensorFlow.
• Experience with data processing tools such as PySpark and cloud-based data platforms.
• Knowledge of entity resolution, data matching and data quality management.
• Experience deploying and monitoring machine learning models in production environments.
• Ability to work with cross-functional teams to translate analytical findings into practical product capabilities.

Educational Background

A degree in Data Science, Computer Science, Software Engineering, Mathematics, Artificial Intelligence or a related quantitative field is required. Relevant professional experience in machine learning, predictive analytics, statistical modelling and applied data science will be an advantage.