Senior Data Scientist, Risk Analytics & ML Leader

ALMR BUSINESS CONSULTING GROUP PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

ALMR Business Consulting Group Pte. Ltd. in Singapore is seeking an experienced Data Scientist to develop machine learning models for credit risk, fraud detection, and data-driven decisions within the financial services sector.

The role involves optimizing models, designing deployment pipelines, and mentoring junior members while ensuring governance and robust documentation. Collaboration with cross-functional teams to translate business needs into analytics is essential for success.

Qualifications

• 5+ years of experience in data science or machine learning, preferably in fintech or financial services.
• Proven experience deploying machine learning models in production.
• Strong knowledge of statistical analysis, predictive modelling, and machine learning.
• Experience in credit risk modelling and model governance is an advantage.
• Familiarity with MLOps and AI-assisted development tools.
• Strong analytical, problem-solving, and communication skills.

Responsibilities

• Develop and optimise predictive models for credit risk, fraud detection, and customer segmentation.
• Analyse complex datasets to identify trends and improve model performance.
• Manage model documentation, validation, governance, and approval processes.
• Build and maintain model deployment pipelines and monitoring frameworks.
• Lead data science initiatives and mentor junior team members.

Skills

Data science

Machine learning

MLOps

Production deployment

Analytical skills

Communication skills

Job description

ALMR Business Consulting Group Pte. Ltd. in Singapore is seeking an experienced Data Scientist to develop machine learning models for credit risk, fraud detection, and data-driven decisions within the financial services sector.

The role involves optimizing models, designing deployment pipelines, and mentoring junior members while ensuring governance and robust documentation. Collaboration with cross-functional teams to translate business needs into analytics is essential for success.