Quantitative Risk Analytics Manager

The role:

As a Quantitative Risk Analytics Manager in our Independent Risk Management function (2LOD), you will play a pivotal role in developing analytics tools and solutions to drive data-driven decision making. You'll collaborate closely with senior leadership to identify opportunities, develop cutting-edge analytical solutions, and deliver actionable insights that will shape our risk oversight strategies.

What you’ll do:

• Partner with senior leaders in the Independent Risk Management function to identify opportunities, develop and implement advanced analytical solutions, and deliver insights to drive improved decision making.
• Design sophisticated statistical and machine learning solutions to support risk oversight, including but not limited to trend analysis, segmentation, decision tree classification, time series analysis, hypothesis testing, natural language processing, and anomaly detection.
• Conduct quantitative analyses using statistical methods, econometric analysis, and other risk management techniques.
• Design, develop, and maintain datasets and pipelines to support risk analytics and dashboards, including data ingestion, transformation, and analysis.
• Leverage industry leading technologies and integrate new data attributes uncover valuable insights and solve complex business problems.
• Serve as a technical subject matter expert, guiding and mentoring data analysts in best practices, business insights, and analytics methodologies.
• Continuously explore new technologies and techniques to improve our analytics capabilities and deliver greater value.

What you’ll need:

• Bachelor’s degree in Computer Science, Engineering, Statistics, Econometrics or a quantitative field required. Master’s or Ph.D. degree preferred.
• 7+ years of impactful experience, preferably within the financial services industry, with significant depth in the methods and analytical details of machine learning and statistical solutions.
• Deep understanding of data analytics methodologies, including statistical analysis, hypothesis testing, data mining, classification, and other advanced statistical and econometrics methodologies
• Proven experience building analytics solutions leveraging Git workflows, modularized coding practices, API calls, and web based solutions. 
• Deep hands-on experience with core technologies such as Airflow, dbt, git, docker, Tableau, Streamlit, AWS, Snowflake, sklearn, TF, statmodels, SQL etc.
• Experience with ML and statistical solutions through all phases of development, from design through training, evaluation, and implementation.
• The ability to analyze complex data, identify patterns, and develop solutions to address business challenges.

Nice to have:

• Notable financial designation(s) such as CFA or FRM