Senior Quantitative Analytics Specialist

In this role, you will:

• Perform highly complex activities related to creation, implementation, and documentation

• Use highly complex statistical theory to quantify, analyze and manage markets

• Forecast losses and compute capital requirements providing insights, regarding a wide array of business initiatives

• Utilize structured securities and provide expertise on theory and mathematics behind the data

• Manage market, credit, and operational risks to forecast losses and compute capital requirements

• Participate in the discussion related to analytical strategies, modeling and forecasting methods

• Identify structure to influence global assessments, inclusive of technical, audit and market perspectives

• Collaborate and consult with regulators, auditors and individuals that are technically oriented and have excellent communication skills

Required Qualifications:

• 4+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

• Master's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science

The Role: 

MRM operates in a fast-paced work environment with continuously changing policies and technologies. The successful candidate as a Senior Quantitative Analytics Specialist is expected to be self- motivated, require minimal supervision, and produce model validation work that is consistent with MRM’s recognized high standards. Effective model validation work will involve familiarity with source systems of record, analytical data and sampling plans, model replications, model performance assessments, and test model development as effective challenges, benchmarking, stress testing, sensitivity analysis of Liquidity and Capital models, Loans, Deposit models, Stress testing models (ILST, LCR), Short term cash forecasting model, recovery and resolution planning models across geographies (US, EMEA, APAC etc.), other forecasting models. It further requires strength in writing detailed standard analytical reports to ensure company’s compliance with governance policies and regulations. Each validation report will include assessments of the specific business, model purpose and history, the model methodology, data integrity, model development, performance, implementation, and monitoring. These documents are read by a broad audience, including auditors and regulators.

In this role, you will:

• Execute the Validation processes based on model risk supervisory guidance, Model Risk Management Policy and procedures, and current industry best-practices in one or more of the above-named areas. In particular:

  • Ensure credible challenge of models through validation process

  • Evaluate all relevant components of models and assess model soundness across lifecycle

  • Identify areas of weakness and work with model owners, risk partners, and other key stakeholders to ensure risk commensurate remediation

  • Demonstrate strong knowledge of subject matter area of focus, as well as sound validation and analysis techniques

  • Deliver high quality and timely validation reports combining intellectual rigor, analytical depth, and key model risk perspective

  • Support timely resolution of model weaknesses

• Develop benchmarking and alternative models in Python/R/Matlab/SAS

• Conducting model replication and review of model documentation

• Managing and mitigating model risk to meet or exceed regulatory and industry standards.

• Providing leadership and consultation to less experienced validators and business partners

• Communicating model risk findings and limitations to key stakeholders

• Contributing to the improvement of model building and model use practices

• Providing analytical support and offering insights regarding a wide array of business initiatives

• Interacting with senior management and regulators on key modeling issues, including the identification, management, and mitigation of model risk

• Proactively following the industry trend to apply and develop new technology as part of R&D to improve efficiency.

• Ensure all models within scope are independently validated per expected standards and schedule

Required Qualification and Skills:

• 4 to 7 years of experience (Masters), 2 to 3 years of experience (Phd) in model validation or model development with Masters / Phd in a quantitative field such as applied math, statistics, engineering, physics, accounting, finance, economics, econometrics, computer sciences, or business/social and behavioral sciences with a quantitative emphasis.

• Able to demonstrate first-hand knowledge of advanced topics in various mathematical and numerical methods such as linear algebra, applied probability, and statistics (Logistic regression, Time Series and Machine learning techniques);

• Familiarity in one or more of the following areas: Liquidity and Capital models, Stress testing models (ILST, LCR), Recovery and resolution planning models, Asset-liability risk management, Deposit models, Predictive modelling with Logistic regression, Time Series and Machine learning techniques.

• Strong mathematical, statistical, analytical, and computational skills along with programming experience in SQL, Python, SAS, R, and comfortable working with large datasets

• Strong communication skills for a variety of audiences (other technical staff, senior management, and regulators) both verbally and in writing

• Capability to multi-task and finish work within strict timelines and provide timely requests for information and follow-up questions

• Ability to work independently on complex model validations from start to finish

• Attention to detail in both analytics and documentation

Desired Skills

• Eagerness to contribute collaboratively on projects and discussions and support the development of high performing teams

• Capability to multi-task and finish work within strict timelines and provide timely requests for information and follow-up questions

• Aptitude for synthesizing data to 'form a story' and align information to contrast/compared to industry perspective.

• Intellectually curious, enjoys solving problems.