ICM In - Business QA - Data Analytics - (VP, data scientist)

In this role, you’re expected to:

Institutional Credit Management’s (“ICM”) objective is to provide an integrated “end-to-end” credit underwriting,  identification, measurement, management, monitoring and reporting for wholesale credit businesses across the enterprise. ICM In-Business Quality Assurance is an integral part of the ICM organization.

The ICM In-Business Quality Assurance function (QA) verifies that established standards and processes are followed and consistently applied. ICM Management uses the results of the quality assurance reviews to assess the quality of the group's policies, procedures, programs, and practices as relates to the management of wholesale credit risk. The results help management identify operational weaknesses, risks associated with the function, training needs, and process deficiencies.

The ICM In-Business Quality Assurance – Data Analytics Vice President, executes and participates on the Quality Assurance Reviews (QARs), providing feedback, insights, and observations on any deficiency and areas of improvement, engaging with the management of the respective ICM business area, Independent Risk, and critical partner stakeholders and regulators.

This role reports to the ICM In-Business Quality Assurance Data Lead, and would ideally be suited to somebody who is currently working in the capacity of a Business Auditor or Risk Management professional with expertise in Machine Learning, Data Science and Analytics with relevant experience in application of analytical tools to Wholesale Credit Risk processes in Global Banking and financial services firms.

Key responsibilities include:

• Support the In-Business Quality Assurance Head of Data Analytics to set the global strategy for and lead the implementation and ongoing delivery of a robust Data analytics and testing program for the Quality Assurance function as it relates to Wholesale Credit Risk (WCR) data governance
• Provide effective challenge on the design and operation of the data and credit processes within ICM and report any identified gaps and concerns on those through quarterly reports published to ICM senior management. Ability to query and clean complex datasets from multiple sources, to funnel into advanced statistical analysis
• Hands-on experience in designing, planning, prototyping, productionizing, deploying, maintaining, and documenting reliable and scalable data science solution(s).
• Deep and hands-on in deriving concrete insights from data and qualifying business impact.
• Good grasp of Wholesale Credit Risk Processes (including Counterparty Credit Risk) and organizational awareness, to evaluate findings identified through the Quality Assurance process, determine materiality, and partnering with business to drive sustainable remediation.
• Perform Design, Operational effectiveness assessments of various business processes within Wholesale Credit Risk with primary focus on Credit Underwriting, Counterparty Credit Risk and Collateral Management
• Develop processes and tools to monitor and analyse model performance and data accuracy
• Provide oversight and guidance over the assessment of complex data related issues, structure potential solutions and drive effective resolution with stakeholders.
• Support WCR IBQA team to abreast of relevant changes to rules/regulations and other industry news including regulatory findings.
• Acts as SME to senior stakeholders and /or other team members.
• Has the ability to operate with a limited level of direct supervision
• Can exercise independence of judgement and autonomy.

As a successful candidate, you’d ideally have the following skills and exposure:

Analytics and Business:

• Demonstrable relevant experience of data analytics, in innovation, modelling and analytics, internal audit, or similar functions at an investment or large commercial bank
• Strong Knowledge of Wholesale Credit Risk (including Counterparty Credit Risk)
• Excellent communication skills with the ability to effectively collaborate with stakeholders at all levels to elicit requirements and priorities.
• Good understanding of Risk and control framework in the banks

Leadership:

• Assists colleagues in identifying stretch opportunities to elevate individual and team performance and recognizes individuals based on performance.
• Continuous learning and improvement mindset.
• Coach and mentor other team members to develop team strengths.
• Proven culture carrier

Competencies:

• Solid organizational skills with ability and willingness to work under pressure and manages time and priorities effectively.
• A logical and methodical mindset, strong analytical and problem-solving skills, with the ability to think critically, working with others to propose creative solutions.
• Leading the delivery of the areas of complex or judgemental QA work, including identifying issues, analysing multiple data points to draw informed conclusions and clearly articulate these conclusions in written and verbal form to senior stakeholders.
• Excellent problem-solving skills with ability to see the big pictures with high attention to critical details.
• Good interpersonal communication skills which will be required for both internal and external business partners.
• Attention to detail and a commitment to delivering high-quality work.
• A drive to learn and master new technologies and techniques.
• Experience in analysing datasets and distilling them into actionable information as well as building out end-to-end analytical process flows.
• Understanding of process redesign / re-engineering and execution
• Experience in preparing presentations for seniors.
• Consistently demonstrates clear and concise written and verbal communication skills.

Technical:

• Coding knowledge and experience with at least two programming languages (C++, Python, C#, Java, R, etc.).
• Experience with deep learning framework PyTorch strongly preferred but not mandatory.
• Experience working and manipulating large set of data.

Qualifications:

• Bachelor’s or Master’s degree in quantitative or equivalent field.
• Data Analysis: SQL; Python; SAS; R, Alteryx, Splunk;
• Visualization: Tableau; Qlikview; MS Power BI.
• Programming language: Python, Java, C++,
• Experience with big data tools: Hadoop, Spark, Kafka, etc

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