Data Scientist

NEURONCREDIT PTE. LTD.; Singapore; On-site; SGD 70,000 - 90,000; Full time

Job summary

A financial services company in Singapore is seeking a Data Scientist to develop and optimize machine learning models for credit and fraud risk. This role involves end-to-end model development, including data exploration, feature engineering, and deployment. The ideal candidate has a bachelor's degree in relevant fields and 2–5+ years of experience in data science. You will collaborate with engineering teams, ensure model governance, and provide analytical insights to enhance portfolio performance.

Qualifications

• 2–5+ years of experience in data science, quantitative analytics, or machine-learning model development.
• Practical experience in Python, SQL, and feature engineering.
• Experience with machine learning techniques such as logistic regression, XGBoost, and random forest.

Responsibilities

• Develop, refine, and maintain statistical and machine learning models for credit risk scoring and fraud detection.
• Build and optimize data pipelines for model development and production deployment.
• Conduct exploratory data analysis (EDA) to uncover business insights.

Skills

Python

SQL

Feature engineering

Logistic regression

XGBoost

Random forest

Scorecards

Model validation

A/B testing

Data pipeline design

Education

Bachelor’s degree in Statistics, Computer Science, Data Science, Mathematics, Engineering, or related fields

Job description

1. Job Summary

We are seeking a Data Scientist to design, build, and optimize machine learning–based credit and fraud risk models across our consumer lending and BNPL product lines. This role involves end-to-end ownership of model development — from data exploration and feature engineering to model training, validation, deployment, and performance monitoring — to enable data-driven decisioning across markets.

2. Key Responsibilities

A. Credit & Fraud Risk Modeling

• Develop, refine, and maintain statistical and machine learning models for credit risk scoring, fraud detection, customer lifecycle prediction, and product performance forecasting.
• Perform deep analysis of large-scale structured and unstructured datasets (e.g., internal behavioural data, credit bureau data, mobile/device signals, e-commerce data).
• Engineer and evaluate large numbers of predictive features to improve model accuracy, stability, and robustness.

B. Data Pipeline & Model Deployment

• Build and optimize data pipelines for model development and production deployment.
• Validate and monitor data quality, feature stability, rank ordering, and score performance across multiple data sources.
• Collaborate with engineering teams to integrate models into production systems (API development, UAT testing, usage monitoring).

C. Analytics & Insights

• Conduct exploratory data analysis (EDA) to uncover business insights and support product/policy decisions.
• Design and run A/B tests to measure model impact and evaluate new credit and fraud strategies.
• Prepare dashboards and reporting frameworks for business and risk stakeholders.

D. Stakeholder Collaboration

• Work closely with product, business, engineering, data, and compliance teams to refine requirements and ensure model alignment with business objectives.
• Provide risk insights and analytical recommendations to enhance overall portfolio performance.

E. Governance & Documentation

• Produce comprehensive model documentation, validation reports, and monitoring materials in line with internal governance and regulatory expectations.
• Ensure all model changes and deployments follow established governance and audit requirements.

3. Required Qualifications

• Bachelor’s degree in Statistics, Computer Science, Data Science, Mathematics, Engineering, or related fields.
• 2–5+ years of experience in data science, quantitative analytics, or machine-learning model development.
• Practical experience in:
• Python, SQL, feature engineering
• Machine learning techniques (e.g., logistic regression, XGBoost, random forest, scorecards)
• Model validation, monitoring, and A/B testing
• Data pipeline design and production deployment

4. Preferred Qualifications

• Experience in consumer lending, BNPL, credit bureau, or digital banking risk analytics.
• Familiarity with model governance frameworks, credit policy, or fraud risk management.
• Experience building automated data workflows or reusable modeling packages.

5. Key Competencies

• Strong analytical reasoning and statistical skills
• Ability to translate data insights into actionable business recommendations
• Detail-oriented with strong model documentation capability
• Collaborative, with excellent stakeholder communication skills