Algorithm developer, C++/Python programmer candidate in Akademgorodok, Novosibirskaya oblast', Russian Federation

Summary

Target position: Data Scientist, applied mathematician, C++/Python software developer.

Examples of task solving:

Code example:
C++
Contact
Python
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Website example:
Contact

Algorithm examples:
Contact
Contact

Experience:

August 2023 - December 2023
Oborontest, Moskow.
Software developer C++/Python.
Development of the client server apps and services with TCPIP, AMPQ, QT, POCO, Pica, etc

December 2021 March 2022
MKK Dengimigom, Naberezye Chelny
Developer of data science algorithms
Developed web service for scoring with linear dependency of distribution of recommended loan sum with known max/min, etc
depending on the measured probability of credit return in time with Flask, docker, Catboost

July 2021 August 2021
Temporary distant job for some private entrepreneur, Kazan, algorithm developer.
Derived the equations and developed the code for estimation the probability and some statistics
for some multivariate distribution.
Software & languages & libraries used: Python, NumPy, MatPlotLib, sftp, ssh, bash.

December 2020 - February 2021
Global Monitoring, Orenburg, AI specialist, distant temporary job.

Developed the analytical algorithm that can detect fuel anomalies (refuel, drain, outliers)/ as I believe, better than
one from here: Contact
Software & languages & libraries used: Python, R, pandas, numpy, scipy, matplotlib, plotly, sftp, ssh, bash.

June 2019 - October 2019
Russian Thermotechnical Institute, Data Scientist, temporary job.

Implementation of a physical models, time series analysis and forecasting.
Development of the library for forecasting the malfunctioning of the power plant equipment.
Algorithms: Linear models, ARIMA, Dynamic harmonic regression, etc.
Software & languages & libraries used: Python, R, Postgresql, Influxdb, Pandas, NumPy, SciPy, Scikit-Learn, MatPlotLib, Plotly.

September 2016 October 2018
NPP TIC, Perm, Programmer (research engineer indeed)
Functions: Planning and carrying out the experiments. Development of the DSP algorithms for vibration-diagnostics, utilizing some data science algorithms. The code for couple of the segmentation algorithms, wavelets (DWT, MODWT, CWT), noise reduction with MODWT, functionality for rotating machinery failure diagnostics with the Continuous Wavelet Transform (CWT), digital filtering, signal decimation, digital measurement of the phase difference with the Zero CRossing with Filtration method, etc

Software & languages & libraries used Matlab, C#, C/C++, Python, R, Pandas, NumPy, SciPy, Scikit-Learn, MatPlotLib, Plotly, Shiny, Accord.NET,

April - June 2016 I worked in ASF GS in Siberian Branch of Academy Of Science
Functions: Soldering of some hybrid(-circuit) boards, mounting and testing some of the experiment automation modules.

November 1996 April 2016
About 11 last years I worked as a programmer, mostly as a freelancer (undocumented). Programming languages/libraries - C++; C#

13 July 1989 21 November 1996
I worked in the Institute of Thermophysics, Novosibirsk 6 years, made mostly hot - wire measurements, some vacuum evaporation also. Main duties selection and development of the experimental environment, planning and carrying out the experiments, processing the experiment results. Ive got 7 scientific publications on hot wire measurements.

Skills:

- Programming experience: 12+ years

- Programming languages: - Python, R, Matlab/Octave, C#, C/C++, Typescript, Javascript.

- Frameworks, tools, etc: WinForms, PyQt/PySide2, Flask, Plotly.js, cvx, cvxpy, docker, microservices, scoring.

- Digital Signal Processing Wavelets, filtering, etc

- Data Science: - Statistics (hypothesis testing, ANOVA, regression analysis, nonparametric statistics), Principal Component Analysis,
regressions - Linear, Ridge, Lasso, Huber, Quantile, Logistic.

- Statistical learning KNN, SVM, Random forest, Cluster Analysis.

- Deep learning: - FC, CNN, LSTM, GANs, Computer Vision, Pytorch.

- Time series analysis & forecasting ARIMA, SARIMA, Time series decomposition (X11, SEATS, STL), Generalized linear models, Exponential Smoothing State Space Model, Regression with ARIMA errors, Dynamic harmonic regression, Hierarchical time series, Vector autoregression, Neural network autoregression, Bagging.

- Physics: - Thermophysics, Aerodynamics.

Etceteras: Latex, Writing scientific articles.

Portfolio:

Portfolio optimization website - full stack web development with Flask, Brython, Typescript, Javascript, Flask Socketio, SqlAlchemy, nginx, uwsgi; website -
asset-master.net

- asset-master.net website highlights:

asset-master.net website can optimize portfolio using arbitrary user data in csv format
asset-master.net website routines uses daily stock data (not yearly or monthly)
asset-master.net website performs processor high - load math routines one by one
asset-master.net website shows task processing log in real time

Convex optimization, PyQT. Portfolio optimization (Markowitz model). Libraries: Cvxpy, PyQt.
Contact

Regression analysis and statistical testing:
Contact

Hypothesis testing:
Contact

FC neural networks with PyTorch (MPL):
Contact

Convolutional neural networks with PyTorch (ResNet implementation):
Contact

Recurrent neural networks, LSTM, attention with PyTorch:
Contact

Generative adversarial networks:
Contact

Forecasts: SARIMA:
Contact

Digital signal processing:
Contact

EDUCATION:
1982 1989
Novosibirsk State University, Physics Department
Specialization: Aerodynamics and hydrodynamics
GPA 4.18

OTHER EDUCATION:
1992 1995
Novosibirsk Electrotechnical Institute, Post graduate
Specialization: Thermophysics and thermodynamics
GPA 4.66

2018 - completed the Stanford StatLearning - SELF PACED Statistical Learning course.

2018 - Studied the Data Science specialization at Coursera first 9 courses of 10 without getting the certificate, My Courseworks & Quizzes can be found here Contact , Contact .

2018 - Studied the Deep Learning specialization at Coursera without getting the certificate.

2019 - Studied the Bayesian Statistics: From Concept to Data Analysis course at Coursera without getting the certificate

2019 - Studied the Bayesian Statistics: Techniques and Models course on Coursera without getting the certificate. Capstone project is here: Contact

2019 Studied the Practical Time Series Analysis at Coursera without getting the certificate. Quizzes are here: Contact

2020 Studied the course EECS 498-007 / 598-005 Deep Learning for Computer Vision: Contact
Assignments are here:
Contact

2020 Completed the courses EE364a, EE364b: Convex Optimization
Contact
Contact

Homework is here:
Contact

2018 Kaggle competitions. My Kaggle rating top 5%. My Kaggle profile - Contact .

LANGUAGES: Russian (native),
English (advanced; Speaking grade (1 5) 3; Reading grade 4)