Igor Stankevich, Developer in Norwalk, CT, United States
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Igor Stankevich

Verified Expert  in Engineering

Deep Learning Developer

Location
Norwalk, CT, United States
Toptal Member Since
March 16, 2020

Igor is a results-driven quantitative development professional with progressive industry experience in commodities, deep learning, computer vision, and data science. He has demonstrated hands-on financial and AI software development experience with in-depth knowledge of Python. Igor consistently attempts to find the best value-add opportunities and contribute to the team as a whole.

Portfolio

Arbol
Amazon Web Services (AWS), Amazon EC2, Amazon S3 (AWS S3), Anaconda, Python...
CVS Health - Data Strategy, Analytics and Enterprise Analytics
Python, Random Forests, Decision Trees, Neural Networks, Spark, Spark ML, SaaS...
Citadel
Beautiful Soup, Jupyter Notebook, PyCharm, Flask, Scikit-learn, NumPy, Pandas...

Experience

Availability

Full-time

Preferred Environment

Jupyter Notebook, Anaconda, Python, Artificial Intelligence (AI), Machine Learning, TensorFlow

The most amazing...

...thing I've developed for a major hedge fund was a business-wide Python modeling framework for quantitative researchers, with cloud risk profiling capabilities.

Work Experience

Head of Pricing and Modeling

2022 - 2024
Arbol
  • Developed risk quantitative framework for derivatives pricing.
  • Created and deployed several (20+) derivatives pricing models.
  • Led a team of seven quantitative developers, four quantitative researchers, AI engineers, and five pricing analysts.
Technologies: Amazon Web Services (AWS), Amazon EC2, Amazon S3 (AWS S3), Anaconda, Python, REST, Risk, Risk Models, Derivatives, Option Pricing, Quantitative Modeling, Quantitative Finance, Development, Quantitative Risk Analysis, Management, Computer Vision, Deep Learning, Artificial Intelligence (AI), Data Scientist, Machine Learning, Research, TensorFlow, Generative AI, Forecasting, APIs, API Integration, Software Development, Python 3, Microservices

Senior Data Scientist

2020 - 2022
CVS Health - Data Strategy, Analytics and Enterprise Analytics
  • Developed deep learning models to capture interactions with CVS customers.
  • Built a Python framework that unified uplift modeling.
  • Developed and deployed uplift models that brought about $10 million of extra revenue.
Technologies: Python, Random Forests, Decision Trees, Neural Networks, Spark, Spark ML, SaaS, Artificial Intelligence (AI), Data Scientist, Natural Language Processing (NLP), Machine Learning, Large Language Models (LLMs), Research, TensorFlow, Generative AI, Data Science, Forecasting, Generative Pre-trained Transformers (GPT), Software Development, Python 3

Senior Quantitative Developer

2016 - 2020
Citadel
  • Designed and developed a modeling and error decomposition framework that enabled interaction across various models and helped to compute errors for each modeling component independently.
  • Developed a futures spreads charting tool, that allowed portfolio managers and analysts to quickly select and pivot various combinations of price time series.
  • Designed and implemented a backtesting framework core, that let analysts use common strategy performance evaluation metrics.
  • Developed web/API scrapes for more than 30 data sources (Pandas, Selenium, beautifulSoup) to feed production models.
  • Converted several fundamental models by restructuring existing code, building new components and rewiring them (Pandas, NumPy, Scikit-Learn, statsmodels, lp_solve).
  • Organized production model runs scheduling, monitoring, and reporting that helped to bring down forecasts’ availability time.
Technologies: Beautiful Soup, Jupyter Notebook, PyCharm, Flask, Scikit-learn, NumPy, Pandas, Python, Artificial Intelligence (AI), Data Scientist, Machine Learning, Research, Forecasting, REST, APIs, API Integration, Software Development, Python 3, Microservices

Biometrics R&D tech Lead

2014 - 2016
Technoserv
  • Developed facial recognition system core with a 99.8% match rate (Chainer/Caffe, Tornado, React), that replaced the legacy system with 75% accuracy and became part of a corporate facial recognition cloud.
  • Managed biometrics team of three developers, focusing on Morpho/Safran/L1 solutions integration.
  • Oversaw and managed biometrics technology deployment and integration into Russian government agencies.
Technologies: C++, React, Tornado, Caffe, Chainer, Scikit-learn, NumPy, Python, Artificial Intelligence (AI), Machine Learning, Research, REST, APIs, API Integration, Software Development, Microservices

R&D tech lead

2011 - 2014
Techno-traffic LLC
  • Developed license plates recognition system using proprietary license plates database (Python, OpenCV, dlib, Tornado, AngularJS), that was integrated into federal highway project on 40 toll lanes.
  • Managed toll roads software development team of 20 developers.
  • Delivered and deployed three layers (embedded, control, and billing) national tolling system.
  • Oversaw video-based vehicle classification system development that replaced 3x more expensive embedded systems.
Technologies: JavaScript, C++, AngularJS, Tornado, Dlib, OpenCV, Scikit-learn, NumPy, Python, Artificial Intelligence (AI), Machine Learning, Software Development, Microservices

Quantitative Developer

2006 - 2010
FX AI Investments
  • Designed and developed a carry-trade system on JPY crosses (served as the main strategy for two years).
  • Implemented more than 50 custom technical indicators for MT4 trading platform (Python, MQL4).
  • Developed the backtesting framework (MQL4 + Matlab) that allowed traders to verify their strategies.
Technologies: Python, MATLAB, MQL4, Forecasting

Commodities modeling framework

• Implemented framework (Python, pandas) that helped to simplify modeling in commodities and rewired existing models via common interface
• Developed complex modeling graphs shared caching strategies (MongoDB/Arctic, Redis, AWS S3) that cut down model run times and enabled quicker collaboration and scenarios exploration between analysts
• Provided support for scenarios management (MongoDB) and execution on a cloud (AWS farm + S3)
• Built an extensive test suite, covering more than 300 functional, integration and performance requirements
• Deployed detailed and intuitive documentation, along with use cases description into centralized system
• Worked with head of directional analysts to design a concept and iterate on core aspects of the framework

Facial recognition cloud

Developed facial recognition system core with 99.8% match rate (Chainer/Caffe, Tornado, ReactJS), that replaced legacy system with 75% accuracy, and became part of a corporate facial recognition cloud.
Development steps:
1. Detailed research on most recent technologies available
2. Core model coding based on academia papers, and training on publicly available dataset
3. Proof of concept (Flask web server + Angular UI): recognizing people on video
4. Core library, trained on proprietary faces database (50 mln faces), with tests and documentation
5. Cloud integration API and scaling

License plate number recognition

Developed license plates recognition system using proprietary license plates database (Python, OpenCV, dlib, Tornado, AngularJS), that was integrated into federal highway project on 40 toll lanes.
Development workflow:
1. Solution design: detection approach, manual features, template segmentation and data aggregation approaches
2. Manual and semi-manual labeling with proprietary and public tools, on a private data set
3. Proof of concept implementation: Tornado server + Angular UI, used proprietary online video (brand new real time data)
4. Optimized application for low-performance lane controllers
5. Deployment

Video based vehicle classification system

Computer vision system to detect and classify vehicles into 4 tolling classes, based on axles count and height. Used variety of computer vision techniques, from background subtraction to Huff transform. Deployed on 200+ toll lanes and replaced 3x more expensive embedded system.

Futures spreads charting application

Led development of a futures spreads charting tool, that allowed portfolio managers and analysts quickly select and pivot various combinations of price timeseries (natural gas contracts):
• Developed vectorized computation library that speeded up spreads and seasonal strips computation tenfold (pandas, numpy)
• Implemented REST backend with load balancing to interact with UI (Flask, gunicorn)
• Led whole application development by iteratively communicating with UI developer and end users, adding/updating requirements, gathering feedback and presenting new functionality
• Deployed “how-to” guides, along with detailed documentation into centralized wiki

Charts pattern recognition library

Developed deep learning based visual pattern recognition library, which helped to automate trading ideas and report signals on hundreds of instruments, helping traders to focus more on detailed research of revealed patterns.
Development workflow:
1. Timeseries conversion into graphical charts, with manual and semi-manual patterns labeling
2. Testing variety of deep learning approaches
3. Model training and fine-tuning
4. Core library and application development
5. Flask server deployment on AWS EC2 instances

Strategy backtesting application

Developed 2 different backtesting applications (in different companies).

Application #1: Strategy testing and optimization framework, with signals generation and performance evaluation on the fly. Framework used Monte-Carlo simulations to pick features' combinations based on the trader-defined universe. These combinations were used to generate series of signals, accounting for patterns' potential biases and create performance report.

Application #2: Core library took holdings and returns series and applied pre-configured metrics to them, generating reports about the strategy, including recommendations based on pre-defined strategy evaluation rules. Library was incorporated into a Flask server, which handled UI requests.

Family Finance App

AWS-based Python application with React front-end, that helps track family finances, connects to external bank accounts, aggregates transactions for different reports, categorizes spendings, and produces a future outlook.

Data acquisition framework

Developed and deployed data acquisition framework (pandas, selenium, requests, beautifulSoup) , which included 3 main steps to get publicly available data sets:
- web scraping or REST API download,
- data cleaning: missing data removal, outliers and inconsistencies detection, manually pre-configured checks
- data checks/alerts: helped to mitigate risk of biased/incorrect forecasts
Framework added a structure to companies data acquisition strategy, which allowed to systematically approach to most of the data feeds business had.
Approach was tested in production on more than 50 data sources, which were feeding production models.
Framework also provided unified data access API for analysts to work with in jupyter notebooks.

Trading and analysis platform integration

Integrated MetaTrader4 trading and analysis platform with python Tornado analysis server, which was performing complex patterns search and generating attention signals.
Implementation steps:
1. Research on the integration approaches from MT4 side
2. Socket protocol implementation based on Twisted (python)
3. Load testing strategies scheduling and constant health checks

Pricing Framework

A risk quantitative framework for weather derivatives pricing uses advanced machine learning, statistics, and probability theory. The framework is written in Python and is used as the main backbone for the company pricing strategy.
2020 - 2022

MicroMasters in Finance

Massachusetts Institute of Technology - Boston

2009 - 2011

Master of Arts Degree in Cognitive Science

Russian State University for the Humanities - Moscow, Russia

1998 - 2004

Master of Science Degree in Economics and Financial Engineering

Moscow Aviation Institute (National Research University) - Moscow, Russia

JANUARY 2022 - PRESENT

MIT MicroMasters in Finance

Massachusetts Institute of Technology

JANUARY 2020 - JANUARY 2023

Certified AWS Solution Architect

AWS

AUGUST 2015 - PRESENT

Scalable Machine Learning

Berkeley on edX

AUGUST 2015 - PRESENT

Big Data XSeries

Berkeley on edX

SEPTEMBER 2012 - PRESENT

Artificial Intelligence

Berkeley on edX

Libraries/APIs

Pandas, Scikit-learn, TensorFlow, TensorFlow Deep Learning Library (TFLearn), NumPy, OpenCV, Flask-RESTful, Beautiful Soup, Dlib, Google API, XGBoost, React, Spark ML

Tools

PyCharm, MATLAB, StatsModels

Languages

Python, MQL, Python 3, MQL4, C++, JavaScript, TypeScript

Platforms

MetaTrader, Amazon Web Services (AWS), Amazon EC2, Anaconda, Jupyter Notebook, AWS Lambda

Paradigms

REST, Microservices, Quantitative Research, Data Science, Management

Storage

Amazon S3 (AWS S3), MongoDB, Redis

Frameworks

Chainer, Flask, Caffe, AngularJS, Apache Spark, MXNet, Spark

Other

Computer Vision, Technical Analysis, Deep Learning, Artificial Intelligence (AI), Machine Learning, Research, Forecasting, APIs, API Integration, Software Development, Commodities, Natural Language Processing (NLP), Financial Markets, Quantitative Modeling, Technical Forex Concepts, Forex Analysis, Tornado, Data Scientist, Generative AI, Equities, Oil & Gas, Fundamental Analysis, Generative Pre-trained Transformers (GPT), Random Forests, Decision Trees, Neural Networks, SaaS, Risk, Risk Models, Derivatives, Option Pricing, Quantitative Finance, Development, Quantitative Risk Analysis, Large Language Models (LLMs), Finance, Statistics, Probability Theory, Modeling, Mathematics

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