Quantitative Analytics Lead

## Technical Skills: Proficiency in quantitative modeling (stochastic and econometric), research, and data analysis. Extensive knowledge in risk management methods (e.g., VaR, expected shortfall, stress testing, backtesting, scenario analysis). Able to read and write code using a programming language (e.g., Python, R, Julia, etc.) in a collaborative software development setting. A high level of proficiency in SQL programming and working with large data sets. Complexity and Impact of Work: Develop and maintain statistical models relevant for market risk, credit risk, and stress testing purposes, including margin, pricing, and VaR applications. Participate in all aspects of the model life cycle, including design, implementation, testing, production, validation, and performance monitoring. Perform quantitative analysis to support business operations and financial forecasts, including data analytics and statistical/econometric modeling. Because of the need to build and mature certain analytics and new products, work is not clearly defined and may lack strategic direction requiring a thoughtful approach. Capable of breaking down large projects and processes into smaller tasks, and accurately estimating their time and scope. Articulate effectively the different options considered, analyze trade-offs, justify and define priorities. Organizational Knowledge: Have a deep knowledge of the strategy of Anchorage Digital and its various business lines. Possess strategic thinking and vision, with the ability to develop and implement a comprehensive quantitative analytics strategy aligned with organizational goals and objectives. Communication and Influence Prepare and deliver clear, accurate, and concise communication orally and in writing for quantitative and non-quantitative audiences. Build effective relationships and rapport with stakeholders including cross-functional partners and external partners. Communicate, organize and execute on cross-team goals and projects, utilizing relationships and resources cross-functionally to solve problems. ## You may be a fit for this role if you have: Masters or Ph.D. degree in a quantitative field such as statistics, applied mathematics, economics, quantitative finance, and operations research. Strong quantitative skills and deep understanding of the following technical areas: 1) financial mathematics (e.g., derivatives pricing models, stochastic calculus, advanced linear algebra); 2) econometrics (e.g., time series analysis, GARCH, copula, etc.) and machine learning techniques; and 3) numerical methods and optimization (e.g., Monte Carlo simulation and finite difference techniques). Direct work experience in financial risk Apply tot his job