Professional with + 6 years of experience in data analysis and risk management. My career began in the banking industry, where worked as a data scientist. From there, transitioned into business and data analysis roles wi…
Professional with + 6 years of experience in data analysis and risk management. My career began in the banking industry, where worked as a data scientist. From there, transitioned into business and data analysis roles within the energy sector. Currently, I am working as a risk analyst in an investment banking, where I apply iterent asset diasses, mainly commodites. fire ughout mysks in career, I have developed a deep understanding of data-driven decision making and risk management strategies, which has allowed me to excel in a variety of roles and industries
Insuring Data quality of time series, detecting anomalies, Outliers, completion & correction of historical Market data
Reconstruction and transformation of historical data of commodities Power, Gas, Oil following methodological models
Reconstruction of volatility historical data of Gas underlyings using data Tick-By-Tick from Ice Provider
Building risk factors KPI's to meet TRIM & FRTB recommendations (Backtesting of RF, shock type review)
Building data quality tool & calibration tool (linear regression) via Python
Calibration of standard deviation & correlation of risk factors
Insuring data quality of time series, detecting anomalies, outliers, completion and correction of historical market data
Reconstruction and transformation of historical data of commodities Power, Gas, Oil following a methodological models
Reconstruction of volatility historical data of Gas underlyings using data tick-by-tick from Ice Provider
Building risk factors KPI's to meet TRIM and FRTB recommandations
Backtesting of RF and shock type review
Building data quality tool and calibration tool (linear regression) via Python
Calibration of standard deviation and correlation of risk factors
Production of econometrical data and estimating the impact of change on financial portfolios VaR/sVaR
Estimation of statistical indicators to control market data (Kupiec and Christoffersen tests, Variance tests)
Business Uses-cases definition of the project P@SS: the petroleum product traceability project (3Millions per year, 40 factory over the world)
Data Analysis in order to suggest target solution anti-counterfeiting (SQL, AZURE, PowerBI)
Elaboration of a Dashboard and Reporting traceability, alerts, quality
Automation of Data processing
Change management (Documentation, Training)
Dashboard Monitoring of data quality (third-party repository) via SAS technology
Data Extraction of the operations from Bank statement using ORC algorithms (Spark, Python)
Storage and data importation via Spark
Automation of the process of banking operation categorization (NLP, Neural Network)
Model Scoring (Detection of the good/bad clients using their banking statements)
Web Scraping of news and publications from several Financial Regulation authorities website (AMF, ACRP...) via Beautiful Soup library Python
Resuming all news and publications and send it under a hebdo newsletter format to users
Pre-processing and classification of news using text mining algorithms
Extraction of new rules via NLP Python
Updating the dashboard with the new rules published and extracted from the financial Regulation authorities
Business uses-cases definition of the project P@SS: the petroleum product traceability project
Data analysis in order to suggest target solution anti-counterfeiting
Elaboration of a dashboard and reporting traceability, alerts, quality
Automation of data processing
Change management (documentation, training)
Web scrapping of news and publications from several Financial Regulation authorities web site (AMF, ACRP...) via Beautiful Soup library Python
Resuming all news and publications and send it under a hebdo newsletter format to users
Pre-processing and classification of news using text mining algorithms
Extraction of new rules via NLP Python
Updating the dashboard with the new rules published and extracted from the financial Regulation authorities
Dashboard monitoring of data quality (third-party repository) via SAS technology
Data extraction of the operations from bank statement using ORC algorithms (Spark, Python)
Storage and data importation via Spark
Automation of the process of banking operation categorisation (NLP, Neural Network)
Model scoring (detection of the good/bad clients using their banking statements)
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