Ilya P.


1105 dollar

My experience


AptaDeepJune 2020 - Present

— Developed MVP: used python/HTML/CSS/Bootstrap to develop SaaS AI platform to replace expensive antibodies with aptamers, using Artificial Intelligence: feature mining from aptamers, NGS data analysis, binding scores prediction, SELEX optimization.
— Coordinated with C-level executives of aptamer companies, secured POC / pilots.

#datascience #python #AWS #linux #mysql #neo4j #docker #pandas #sklearn #keras #deeplearning #websites #datascraping

Open Data Science ParisJanuary 2019 - Present

— Build a strong data science community that meets every week.
— Great data science ecosystem with access to variety of deep expertise: including accomplished researchers, math olympiad winners, strong competitive data scientists.

Entrepreneur FirstApril 2020 - August 2020

— Was one of 3% of applicants selected to join EF.— Provided weekly reports to Entrepreneurs in residence and VC partners and eventually pitched to the investment committee for pre-seed funding.— Topics: Business Models, Financial Modelling, B2B Sales, OKRs, Market Sizing, Competition and Defensibility Analysis, Early Stage Growth, Fundraising, Investor Decks, Venture Economics, Communication, Customer Development.

newspillApril 2019 - March 2020

— Stock market volatility prediction by machine learning applied to anomaly indicators extracted from multiple types of data (scrapped data of internet chatter, technical data, contextual data) and redesign of the legacy algorithmic trading system.
— Supervised Data Science powered case studies such as Trump Mood Predictor.
— Infrastructure management: AWS, docker, redis, SQL, python, flask, gunicorn, nginx, gitlab.
— Build a chatbot framework from scratch for easy creation of rule-based chatbots.
— Managed / mentored interns to maximize their value for the company and their careers.
— Conducted job interviews and developed a score-based screening system.

— 🏆Pitched the startup and contributed to securing funding with BPI & Rockstart AI, startup featured at BFM Business TV channel.

#datascience #timeseries #options  #datascrping #dataengineering #AWS #redis #SQL #flask #gunicorn #nginx #gitlab #docker #communication #fundrising #chatbot


Dataswati AI for Manufacturing and Data-Driven IndustriesJanuary 2018 - May 2019

— Predictive models for unevenly sampled time-series with uncertainty quantification.
— Built automated data pipelines: from raw data to automated cross-validation based feature generation and selection to predictions.
— Deep Learning approaches: CNN, LSTM, auto-encoders, transfer learning.
— Customized implementations: optimization by Differential Evolution, a causal model of regime change, Wasserstein distance based anomaly detection, and a new method of multi-domain transfer learning.
— Tech evangelism to promote the company: blog on, talks at Meetups.
— Set up collaborations with academic researchers at INRIA.

#deep-learning #time-series #datapipelines #transfer-learning #optimal-transport #auto-encoders #lstm #cnn #python


Self-EmployedFebruary 2017 - December 2017

— Researched synaptic plasticity exposed to randomized input patterns using Monte-Carlo numerical simulations. Collaboration with researchers at Collège de France and INRIA.

InriaOctober 2013 - December 2016

— Developed a Data-Driven Mathematical Model which explained the dependence of synaptic learning on the activity of neurons and experimental conditions. See
— Worked with various experimental and synthetic datasets: Data Cleaning, Parsing, Transformation and Modeling.
— Numerical Stochastic Simulations of Differential Equations, Parameter Optimization, Sensitivity Analysis.
— Python for Data Analysis (NumPy, SciPy, PANDAS, sklearn, and matplotlib) and Numerical Optimization (PyGMO); Numerical Integration in FORTRAN95 interfaced with Python using f2py (x100 faster than Python+SciPy+NumPy).
— 1 scientific publication (eLife, top 10% journal in biology/neuroscience), 2 submitted, 1 in preparation.

Institute of Applied Physics of Russian Academy of SciencesJanuary 2011 - January 2013

— Processing 64-dimensional time-series data recorded from neuronal cultures grown on multi-electrode arrays.
— Developed a method for graph reconstruction from the time-series data generated by graph's nodes.
— Time-series correlation and its statistical significance in C++; data manipulation/visualization in MATLAB.

State University of Nizhni NovgorodJanuary 2009 - January 2013

— Solved numerically Differential Equations based model of a Neural Network with a customized Runge-Kutta in C++.
— 2 international scientific publications describing the model of interacting neurons and an adaptive synapse.

My stack

IT Infrastructure



Fortran, Cython, C/C++, C++, LaTeX, Haskell, Python, SQL, Matlab


Software Engineering, NumPy, Machine Learning


Data Science, Data analysis, Communication, Artificial Intelligence, Natural Language Processing (NLP), Algorithms

Machine Learning

Deep learning, Neural networks

My education and trainings

Data Science Summer School (DS3) - Data Science - Polytechnic University2017

Doctor of Philosophy - National Institute of Applied Sciences2013 - 2016

Master's degree - Physics - State University of Nizhni Novgorod named after N.I. Lobachevsky (UNN)2011 - 2013

Bachelor's degree - Physics - State University of Nizhni Novgorod named after N.I. Lobachevsky (UNN) State University of Nizhni Novgorod named after N.I. Lobachevsky (UNN)2007 - 2011