Total 2 data entry job vacancies in Finance / Banking / Insurance category in D18 Tampines, Pasir Ris

Working Location: Central

Working hours: 10am to 6pm (Monday to Friday)

Your role:

• Consolidate and analyze financial data (budgets, income statement forecasts etc.) taking into account company’s goals and financial standing
• Provide creative alternatives and recommendations to reduce costs and improve financial performance
• Assemble and summarize data to structure sophisticated reports on financial status and risks
• Develop financial models, conduct benchmarking and process analysis
• Conduct business studies on past, future and comparative performance and develop forecast models
• Identify trends, advise company and recommend actions to a senior financial analyst based on sound analysis
• Track and determine financial status by analyzing actual results in comparison with forecasts
• Reconcile transactions by comparing and correcting data
• Gain and update job knowledge to remain informed about novelty in the field
• Consult with management to guide and influence long term and strategic decision making within the broadest scope
• Drive process improvement and policy development initiatives that impact the function

Requirements

• Minimum Diploma and above
• Prior experience in the marketing field will be an advantage
• Results-oriented, ambitious and positive working attitude

EA Reg Number: R2094147

EA License Number: 22C1278

About Us

We're seeking a driven and skilled Credit Risk Data Scientist (1 year contract) to join our dynamic team. If you're passionate about predictive modeling, risk management, and leveraging cutting-edge technologies, this role offers an exciting opportunity to contribute to the development of credit scoring and risk mitigation strategies.

Responsibilities

• Develop, maintain, and monitor predictive models for credit scoring, fraud detection, and suspicious activity prediction.
• Write high-quality production-ready code for the models that will be deployed in the production environment.
• Deploy the models in the MLOps production environment, and maintain and troubleshoot any production issues that arise with these models.
• Collaborate closely with Lending and Credit Risk teams to translate requirements into optimized models.
• Support regular model performance reviews, audits, and recalibrations.
• Collect, process, and analyze data to support statistical analysis and risk decision-making.
• Leverage alternative data sources to enhance underwriting and credit risk models.
• Present model concepts and metrics to senior stakeholders and validators/auditors.
• Design, implement, and maintain performance reports and actionable insights dashboards.
• Ensure alignment with model governance, business, and regulatory expectations.

Experience and Qualifications

• Degree (PhD, Master's, Bachelor's) in Statistics, Mathematics, Economics, Finance, Risk Management, or related field.
• Proven experience building risk models such as Application Score, Behavior Score, PD, EAD, LGD (Basel or IFRS 9), especially for retail customers in diverse countries including Singapore.
• Comprehensive understanding of risk management throughout the customer journey within fintech or risk-focused environments.
• Domain expertise in retail credit products, banking, and regulatory frameworks (Basel, IFRS).
• Proficiency in Python, R, SQL, Snowflake and Data Visualization tools like Tableau.
• Technical proficiency in data retrieval, data modeling, data mining, and predictive modeling.
• Strong familiarity with advanced machine learning techniques, statistical classification/regression for credit risk.
• Exceptional problem-solving skills, detail-oriented mindset, and ability to thrive in a dynamic work setting.
• Bonus: Familiarity with implementing models using Terraform scripts, Docker/Kubernetes, CI/CD technologies, AWS, or MLOps.

Why Join Us

• Make a significant impact on credit risk strategies and predictive modeling.
• Collaborative and innovative work environment.
• Opportunity to work with cutting-edge technologies and diverse datasets.
• Continuous learning and professional growth opportunities.
• Competitive compensation package and benefits.