Michael Alexander Ramírez Sierra

Experience

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  UBS

  Oct 2016 - Mar 2017

  Quantitative Data Analyst - Securities-Backed Lending Team

  Intern* Applying techniques from numerical analysis and financial mathematics to recognize credit risk sources.* Supporting capital asset pricing model (CAMP) stress testing and benchmarking to detect portfolio weaknesses.* Interacting with global teams to share insights from ad hoc business-data analyses and investigations.* Automating risk model validation workflows across all major asset classes.

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  PwC Polska

  May 2017 - Jul 2017

  Anti-Money Laundering (AML) Analyst - Global Compliance Management Services

  Financial Crime Unit* Exploring suspicious financial activity data to investigate and uncover fraudulent banking behavior.* Employing popular data science tools such as PCA and logistic regression to identify financial crime risk drivers.* Developing transaction monitoring solutions to manage AML schemes for complex business environments.

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  UBS

  Aug 2017 - Aug 2020

  Market Risk Data Analyst - Market Risk Methodology and Backtesting Team

  Value-at-Risk (VaR) Modelling and Analytics Specialist* Advancing VaR modeling methodology via stochastic mathematical frameworks.* Collaborating timely across departments to deliver market risk assessments.* Building R Shiny dashboards and data-driven analytics to enhance business decision-making.* Performing VaR time series forecasting, market data quality control, and VaR model recalibration.* Documenting VaR model and data improvements to effectively inform internal stakeholders. Show less

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  FIAS - Frankfurt Institute for Advanced Studies

  Sept 2020 - now

  Research Assistant - Center for Multiscale Modelling in Life Sciences (CMMS)

  Theoretical Biophysics* Integrating strategically physics, mathematics, and statistics to simulate complex systems.* Applying AI/ML algorithms such as GMM clustering to analyze big data sets.* Exploiting deep-learning techniques such as MAF density estimation to perform model parameter inference.* Leveraging high-performance computing (HPC) to quantify model reliability and robustness.* Communicating periodically complex insights to multidisciplinary audiences.