Data Scientist

EXCELTECH COMPUTERS PTE. LTD.; Singapore; On-site; SGD 90,000 - 120,000; Full time

Job summary

EXCELTECH COMPUTERS PTE. LTD. in Singapore invites a Data Scientist to join the Fraud Analytics Models (FAM) team within the Data Science and AI Division.

The role focuses on maintaining and expanding FAM 2.0, addressing methodology and threshold review backlog, and operationalising the FAM 2.0 Technical Dashboard. You will assess how new schemes can be brought within FAM 2.0's fraud-risk coverage, work with large datasets, and contribute to model validation and analytics initiatives with 3–5

Qualifications

• 3–5 years hands-on experience in data science, advanced analytics, model validation, fraud/risk analytics or programme analytics.
• Experience developing, validating or enhancing predictive, risk-scoring, fraud-detection or anomaly-detection models.
• Ability to translate business requirements into analytical specifications and indicators.

Responsibilities

• Maintain and expand FAM 2.0 within Fraud Analytics Models team.
• Address methodology and threshold review backlog.
• Build and operationalise the FAM 2.0 Technical Dashboard.
• Assess how new schemes can be progressively brought within FAM 2.0's fraud-risk coverage.

Skills

Python

SQL

Job description

Data Scientist

Work location- Paya Lebar

The Data Scientist is embedded within the Data Science and AI Division (DSAD), working as part of the Fraud Analytics Models (FAM) team. The role will support the maintenance and expansion of FAM 2.0, which detects misuse of public training grant. This includes addressing the methodology and threshold review backlog; building and operationalising the FAM 2.0 Technical Dashboard; and assessing how new schemes can be progressively brought within FAM 2.0's fraud-risk coverage.

Experience: 3-5 years of hands-on experience in data science, advanced analytics, model validation, fraud/risk analytics or programme analytics.

B. Technical & Professional Competencies

• Strong hands-on proficiency in Python and SQL for data extraction, transformation, exploratory analysis, with experience building reproducible analytical workflows.
• Hands-on experience developing, validating or enhancing predictive, risk-scoring, fraud-detection or anomaly-detection models, including feature development, back-testing, threshold calibration and performance evaluation using measures such as precision, recall and related trade-offs.
• Experience translating business, policy or operational requirements into analytical specifications, model logic, indicators, rules or measurable risk hypotheses.
• Experience working with complex, large-scale datasets, including assessing data quality, tracing data lineage, investigating anomalies and documenting analytical assumptions.