Risk Analytics Head

City Savings Bank; Pasig; On-site; PHP 1,800,000 - 3,000,000; Full time

Job summary

City Savings Bank is seeking a Risk Analytics Head to lead advanced analytics and modeling initiatives across risk areas. You will develop and enhance score models, implement ML/AI approaches, and work with the technology team to establish ML Ops pipelines and automated monitoring of models.

Responsibilities include developing risk measurement methods, building scoring models, and delivering insights that inform risk-based decisions.

Qualifications

• 5-8 years total working experience in the business analytics or risk analytics domain.
• Experience with scorecards and risk modeling across credit, market, and operational risks.

Responsibilities

• Develops, validates, and enhances risk measurement methods across risk types.
• Build, validate, and deploy scoring models and related policies.
• Design econometric and statistical models for projections, classification, clustering, and simulations.
• Establish ML Ops pipelines for rapid rollout and automated monitoring.
• Collaborate with CRO to present insights to stakeholders.
• Automate data extraction and preprocessing; document methodologies.
• Provide actionable insights to improve risk-based decisions.

Skills

Advanced analytics

Statistical modeling

AI/ML techniques

Risk analytics

Data mining

Communication with CRO

Education

Bachelor's degree in Statistics, Mathematics, or related field

Tools

Python

R

SQL

ML Ops

Job description

The Risk Analytics Head is a key role in the risk organization responsible for advanced analytics and modeling initiatives within City Savings Bank. She/ He will be responsible for developing and enhancing score models across multiple risk areas using traditional methods as well as new AI/ML techniques. Working with the tech team she/he will lay out the ML Ops pipelines and structure for rapid rollout and automated monitoring of models.

Job Specifications:

• Develops, enhances, and validates the methods of measuring and analyzing risk, for all risk types including market, credit and operational.

• Develop, validate and strategize uses of scoring models and scoring model related policies.

• Build econometric and statistical models for various problems inclusive of projections, classification, clustering, pattern analysis, sampling and simulations

• Build the foundation of state-of-the-art scientific and technical capabilities within the Data Science department in order to support several planned and ongoing data- analytics projects.

• Provide forward-thinking recommendations to risk management by building in-depth understanding of the domain and available business data assets, especially those pertaining to credit, fraud, collections, operational, market and liquidity risk

• Execute ad-hoc data mining and exploratory statistics tasks on large data-sets related to the business 'strategies.

• Generate actionable insights applying advanced statistical techniques, for example, predictive statistical models, segmentation analysis, customer profiling, analysis, survey design, and data mining

• Collaborate with the CRO to communicate obstacles and findings to relevant stakeholders in an effort to improve risk-based decision making and drive business performance.

• Conducts statistical analysis for risk related projects and data modeling/validation.

• Prepares statistical and non-statistical data exploration, validate data, identify data quality issues.

• Conducts data analysis, data mining, read and create formal statistical documentation, reports and work with Technology to address issues.

• Analyzes and interprets data reports, make recommendations addressing business needs.

• Uses Predictive modeling methods, Optimizing monitoring systems, document optimization solutions, and present results to non-technical audiences; write formal documentation using statistical vocabulary.

• Generates statistical models to improve methods of obtaining and evaluating quantitative and qualitative data and identify relationships and trends in data and factors affecting research results.

• Validates assumptions; escalated identified risks and sensitive areas in methodology and process.

• Automates data extraction and data preprocessing tasks, perform ad hoc data analyses, design and maintain complex data manipulation processes, and provide documentation and presentations.

Qualifications:

• At least 5 - 8 years total working experience in the business analytics or risk analytics domain (preferred)