Jean-Michel Nairac, Developer in Grand Gaube, Rivière du Rempart District, Mauritius
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Jean-Michel Nairac

Verified Expert  in Engineering

Data Scientist and Developer

Grand Gaube, Rivière du Rempart District, Mauritius
Toptal Member Since
December 27, 2021

Jean-Michel is a senior data scientist and quantitative risk manager with 8+ years of experience. He worked at PwC and CIEL Group Companies, where he also gained his consulting experience, strong business acumen, and leadership skills. Jean-Michel's industry experience is backed by bachelor's degrees in economics and statistics and a master's degree in financial mathematics. He is currently ranked in the top 2% of Kaggle competitions.


CIEL Finance Data Services Ltd
Python, Pandas, Azure Computer Vision API, Azure Machine Learning, SQL...
Loop Shops Ltd (Self-employed)
PrestaShop, Web Hosting, Zoho CRM, Online Marketing, Supply Chain, eCommerce...
CIEL Corporate Services Ltd
Risk Management, Financial Risk Management, Operational Risk, Market Risk...




Preferred Environment

Python, Microsoft Azure, Scikit-learn, LightGBM, XGBoost, TensorFlow, Pandas, NumPy, SQL

The most amazing...

...machine learning model I've built predicts the credit scores of loan applicants. I also designed the data pipeline to automate the loan approval process.

Work Experience

Lead Data Scientist

2020 - PRESENT
CIEL Finance Data Services Ltd
  • Built a machine learning model that predicts the credit scores of loan applicants for a bank.
  • Developed the model in Python with LightGBM, TensorFlow, scikit-learn, and Pandas and designed the data pipeline to automate the loan approval process in Microsoft Azure.
  • Built an anomaly detection tool that uses machine learning to automatically detect anomalies and data quality issues in bank transaction data. I developed this in Python with the PyOD package.
  • Used computer vision and optical character recognition in Microsoft Azure to extract electricity meter readings from security image capture.
  • Worked closely with clients to strengthen their understanding of data science, artificial intelligence, and machine learning to help them identify viable ML use cases for their businesses and present their business cases to C-level executives.
  • Collaborated with data engineers to understand data and data infrastructure requirements to put machine learning models into production for every use case. This involved daily standups and sprint planning using Jira.
Technologies: Python, Pandas, Azure Computer Vision API, Azure Machine Learning, SQL, Azure SQL, Keras, Machine Learning, Deep Learning, Data Science, Predictive Analytics, Commercial Banking, Finance, Statistical Methods, Scikit-learn, LightGBM, XGBoost, Anomaly Detection, Principal Component Analysis (PCA), Azure Data Factory, TensorFlow, Algorithms, Statistical Data Analysis, Data Analysis, Microsoft Power BI, Data Visualization, Dimensionality Reduction, Jira, Data Analytics, Leadership, CatBoost

Executive Director

2018 - 2021
Loop Shops Ltd (Self-employed)
  • Mastered PrestaShop and leveraged it to become an eCommerce developer and online marketing specialist.
  • Built an eCommerce website for selling organic food items locally in Mauritius (
  • Gained entrepreneurial, sales, and CRM experience by creating this startup and building relationships with multiple local vendors.
Technologies: PrestaShop, Web Hosting, Zoho CRM, Online Marketing, Supply Chain, eCommerce, Data Analysis, Data Analytics, Leadership, Director

Head of Risk

2017 - 2020
CIEL Corporate Services Ltd
  • Built a financial risk model that quantified the organization's risk appetite. This included deriving the expected financial return for the organization and each of its subsidiaries.
  • Won the Best Risk Management Report award at the 2017 PwC Corporate Reporting Awards in Mauritius.
  • Designed and implemented an overall risk management process for the organization, which required expertise in healthcare, textiles, agriculture, finance, real estate, and hospitality.
  • Supervised and trained risk officers, built and presented reports to C-level executives and board members, and ran risk awareness campaigns for large audiences.
  • Supported the organization's risk management efforts through COVID-19 and developed and executed a pandemic response plan.
Technologies: Risk Management, Financial Risk Management, Operational Risk, Market Risk, Credit Risk, Financial Modeling, Statistical Data Analysis, Data Analysis, Data Analytics, Leadership

FX & Derivatives Trader

2016 - 2017
Afrasia Bank Ltd
  • Built a machine learning model in Python that predicts hourly foreign exchange fluctuations in the EUR/USD FX rate. I developed this model to showcase the power of machine learning and algorithmic trading for the bank.
  • Used technical and fundamental analysis to trade FX and FX derivatives at the treasury desk.
  • Designed FX-related structured products for clients.
Technologies: Forex Trading, Structured Products, Machine Learning, Python, Statistical Data Analysis, Data Analysis, Data Analytics, Algorithmic Trading

Data Scientist

2015 - 2015
DataProphet Ltd
  • Built an agent-lead matching model for call centers. We used a gradient boosting machine to predict which agents would be most suitable to handle call leads that yield the highest conversion rate built-in R with the GBM package.
  • Assisted the sales team in sourcing leads and onboarding clients with the benefit of CRM tools like Pipedrive.
  • Built machine learning models for debt collection and financial services companies built-in Python with scikit-learn.
Technologies: Python, R, Machine Learning, Data Science, Customer Relationship Management (CRM), Data Analysis, Data Analytics

Actuarial, Risk, and Quant Consultant

2013 - 2015
  • Applied financial engineering and quantitative finance techniques to build financial and credit risk models and value financial derivatives.
  • Valued and reviewed risk-weighted bank assets in accordance with Basel II and III. Consulted with major banking clients and reported and presented to C-level executives.
  • Performed actuarial reviews, including IAS 19 employee benefit reviews.
Technologies: Financial Modeling, Financial Engineering, Machine Learning, R, Python, SAS, SQL, Credit Risk, Market Risk, Risk Models, Quantitative Finance, Consulting, Basel II, Basel III, Asset Valuation, Statistical Data Analysis, Data Analysis, Data Analytics, Quantitative Analysis, Quantitative Modeling

Automation of Loan Approvals

A bank client engaged Ciel Finance Data Services Ltd to automate its loan application process, which was manual and tedious. Loans were pre-approved based on a simple checklist, and final approval was a qualitative exercise reviewed by multiple committees.

1. Design a new loan application process.
2. Build ML models to produce a credit score for loan approval.

I designed a new data pipeline that involved:
• An ETL process of the data into Microsoft Azure.
• A Python script to execute the ML models and predict the credit scores.
• A SQL database to store the results and push them to the client via email.
• Azure Data Factory to orchestrate and automate the process.

I built two ML models and combined them to produce a credit score. One model predicts the probability that the committees will reject the application, and the other predicts the probability of default given that the committees approved the loans. Each model uses deep learning (TensorFlow and Keras), and gradient boosted trees (LightGBM and XGBoost). Features were engineered with statistical methods using Pandas, SciPy, and NumPy.

We saved the bank a lot of time by discontinuing several committees and improving its loan default rate.

Anomaly Detection Tool for Bank Transactions

An anomaly detection tool I built to detect anomalies in bank transaction data. The tool is an ensemble machine learning model of many models with various hyperparameters of KNN, HBOS, Isolation Forest, PCA, and COPOD models.

The client did not have a system to detect anomalies, so they used only ad hoc samples.

I engineered research-backed features for anomaly detection in bank transaction data. The data pipeline involves an ETL process of incrementally loading data into Microsoft Azure SQL Database using Azure Data Factory. A Python script containing the anomaly detection tool is automatically executed on the incrementally loaded data, and a CSV file containing anomalies is sent directly to the client by email.

The client is using my anomaly detection tool because:
• It can run all transaction data as opposed to a sample.
• It automatically filters outliers, which is a vast improvement over the manual process.
• It provides an outlier score so that the client can prioritize the investigation of potential anomalies as they see fit.

The client confirmed that the tool is effective in detecting more data quality issues and fraudulent activities than the ad hoc sampling process.

Predicting Volatility for Options Trading
This was a Kaggle competition to predict volatility for options trading.

We received trade data and order book data in 10-minute intervals per second, totaling over one billion data points. Our task was to predict 10-minute volatility with performance assessed using root mean squared percentage error (RMSPE) between true and predicted volatility.

I performed well in this competition because I:
• Applied data analytics to correctly handle errors, such as trading halts, stock splits, mergers, and low trading volumes.
• Used the correct cross-validation technique (StratifiedGroupKFold).
• Engineered research-backed features (papers on algorithmic trading are freely available on Google Scholar and arXiv).
• Re-engineered timestamps and captured correlation between timestamps.
• Engineered features that correctly captured the correlation between stocks (Pandas and NumPy).
• Used ensemble methods of linear models (scikit-learn), gradient boosted trees (LightGBM), and deep learning (TensorFlow and Keras).

Computer Vision and OCR of Meter Readings

The client manually recorded electricity meter readings. This tedious process consumed a lot of the client's resources because many meter readings had to be recorded on a regular basis.

We proposed using security cameras to capture images and automatically extract the meter readings using artificial intelligence and machine learning.

We used Microsoft Azure tools to build the solution and the custom vision software to detect objects—meter screens from security image captures. Microsoft also provides Computer Vision OCR for text extraction from images. We combined these into a SaaS solution to detect meter readings from security images.

The pipeline is engineered as follows:
1. The security image captures are uploaded to Azure Blob storage.
2. This triggers a Python script that makes an API call to the custom vision predictions and another API call to the Computer Vision OCR.
3. Meter readings are extracted and pushed to an Azure SQL database.

The client saves a lot of time, and resources are freed up to focus on other tasks.
2012 - 2013

Master's Degree in Financial Mathematics

University of Cape Town - Cape Town, South Africa

2007 - 2011

Bachelor's Degree in Statistics

University of Cape Town - Cape Town, South Africa

2007 - 2010

Bachelor's Degree in Economics

University of Cape Town - Cape Town, South Africa


Professional Risk Manager (PRM)



Pandas, XGBoost, CatBoost, Scikit-learn, Keras, TensorFlow, NumPy, Azure Computer Vision API, SciPy


PrestaShop, Microsoft Power BI, Azure Machine Learning, MATLAB, Jira




Python, R, SQL, SAS


Data Science, Anomaly Detection, Quantitative Research


Azure SQL, Azure SQL Databases, Data Pipelines, Azure Cosmos DB


Zoho CRM, Windows, Director


Feature Engineering, Data Cleansing, Statistical Methods, Finance, Commercial Banking, Credit Risk, Operational Risk, Risk Management, Financial Risk Management, Financial Modeling, Financial Engineering, Risk Models, Statistical Data Analysis, Data Analysis, Dimensionality Reduction, Data Analytics, Quantitative Analysis, Model Validation, Ensemble Methods, Machine Learning, Principal Component Analysis (PCA), Predictive Analytics, Options Trading, Market Risk, Online Marketing, eCommerce, Quantitative Finance, Time Series Analysis, Algorithms, Numerical Analysis, Variational Autoencoders, Azure Data Factory, Artificial Intelligence (AI), OCR, Deep Learning, Web Hosting, Supply Chain, Forex Trading, Structured Products, Calculus, Econometrics, Asset Valuation, Mathematics, Statistics, Operations Research, Markov Chain Monte Carlo (MCMC) Algorithms, Generalized Linear Model (GLM), Numerical Methods, Probability Theory, Stochastic Process, Multivariate Statistical Modeling, Bayesian Statistics, Decision Modeling, Portfolio Analytics, Economics, International Trade, Macroeconomics, Microeconomics, Microsoft Azure, Customer Relationship Management (CRM), Consulting, Basel II, Basel III, Gradient Boosted Trees, Predictive Modeling, Computer Vision, Data Visualization, Algorithmic Trading, Backtesting Trading Strategies, Leadership, Quantitative Modeling

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