Skills

Python

Very good (6+ years)

R

Very good (4+ years)

C#/.NET

Very good (4+ years)

ML tools

Scipy/Tensorflow/Theano/NLTK/SpaCy/LightGBM/etc.

R statistical tools

forecast/mlr/caret

Cloud/Big data tools

Apache Spark / Cloudera / Impala

Experience

 
 
 
 
 

Senior Data Scientist

Capgemini

Nov 2020 – Present Wroclaw

As a Senior Data Scientist, I’m responsible for delivering high-quality analysis and machine learning models for external customers and being an internal consultant and technical leader for some projects. My main areas of expertise in a Company are applications of:

  • neural network models,
  • reinforcement learning,
  • time series forecasting,
  • anomaly detection.

The current technology stack includes Python data science toolset (pandas, NumPy, scipy, scikit-learn), neural networks technologies (Tensorflow, Keras, Pytorch), visualization libraries (Streamlit, Dash), and R/Rstudio for statistical/scientific analysis.

 
 
 
 
 

Data Scientist

Nokia

Mar 2020 – Sep 2020 Wroclaw

Main tasks:

Data Scientist in Commercial Management and Business Digitalization Department. Main tasks:

  1. Building predictive models to support business processes in various areas - supply chain management, cost/price analysis, etc. Using multiple machine learning and statistical techniques:
  • Time series analysis
  • Regression, ANOVA
  • Machine learning models (deep learning, gradient boosting, model ensembles)
  1. Building optimization engines for supply chain management

  2. Preparing research papers and academia workshops related to the Teams’ activity

  3. Core technologies:

  • Python scientific environment: scipy, NumPy, scikit-learn, pandas
  • Optimization engines: cvxpy with different solvers, Matlab
  • Neural networks toolset: Tensorflow, Tensofboard, GPU computing
  • Databases: MSSQL
  • Interactive dashboards: MS PowerBI, Python Streamlit
 
 
 
 
 

Senior Data Scientist

Objectivity

Nov 2017 – Feb 2020 Wroclaw
Main tasks:

  1. Building complex and scalable machine learning algorithms for The Clients, from various industries. Data Science areas include:
  • Recommendation systems
  • Natural language processing
  • Deep Learning
  • Financial forecasting and prognosis
  • Decision support systems design and implementation
  1. Main data science techniques:
  • Classic machine learning - classification/regression/clustering with decision trees, rule-based systems, xgboost, lgbm, catboost, SVM etc.
  • Econometric techniques - forecasting with (S)ARIMA(X), ETS, dynamic regression
  • Neural networks and deep learning - image recognition, classification, LSTM using TensorFlow, Keras, Theano
  • Statistical analysis - ANOVA, ANCOVA, hypothesis testing
  • Interactive dashboards
  1. Core technologies:
  • Python scientific environment: scipy, numpy, scikit-learn, pandas
  • R environment: caret, xgboost, forecast
  • Neural networks toolset: Tensorflow, Keras, Theano
  • Databases: MSSQL, MongoDB, ElasticSearch, Postgres
  • Interactive dashboards: MS PowerBI, Tableau
 
 
 
 
 

Data Scientist, Assistant Vice President

Credit Suisse

Nov 2016 – Oct 2017 Wroclaw

As the Assistant Vice President of FX Sales Analytics department, developed custom sales analytics application in big data environment for recognizing trading patterns and grouping clients using their portfolio or preferences. Time-series decomposition tools for trend forecasting as well as natural language processing solution – recognizing topics in media feeds.

Technologies:

  • Cloudera stack(Hadoop distribution)
  • Scala + Spark + Spark Streaming + mllib
  • R + caret + mlr + xgboost + H2O
  • H2O ai
  • Python + scipy + pandas + sklearn
  • Pig
  • Jupyter notebooks stack

Databases:

  • Impala
  • Hive
  • MS SQL Server
  • Informatika Power Center
 
 
 
 
 

Business Analyst

Opera Software

Aug 2016 – Oct 2016 Wroclaw

Delivering analysis of users’ characteristics as well as their post-release behavior. Complex A/B testing and statistical survival analysis (churn/retention). Monte Carlo simulations for predicting future behaviors (reaction to new features). Direct cooperation with Company Management.

Technologies:

  • Cloudera stack(Hadoop distribution)
  • R + caret + mlr + xgboost + H2O
  • H2O ai
  • Python + scipy + pandas + sklearn
  • Pig
  • Jupyter notebooks stack

Databases

  • Impala
  • Hive
 
 
 
 
 

Senior Software Developer

Luxoft

Sep 2014 – Jul 2016 Wroclaw
Responsible for delivery of sales analytics application as well as customer behavior analyzer in big data environment. Grouping clients based on their preferences as well as trading patterns. Complex financial forecasting. Creation of ETL pipelines for big data cluster.
 
 
 
 
 

software developer

Anixe

Sep 2012 – Aug 2014 Wroclaw
Development of recommendation engines for trip planning and hotel booking. Creation of solutions for near-real time route optimization, as well as natural language processing algorithms for entity recognition (hotels) from descriptions, comments and reviews. ETL processes for cleaning Booking.com documents and storing them in MongoDB. Direct cooperation with the Clients.
 
 
 
 
 

software developer

eActive

Feb 2012 – Jun 2012 Wroclaw
Delivering decision support systems for SEO – analysis of positions and customer revenues. Forecasting company’s profitability.

Recent & Upcoming Talks

Using autoencoder neural networks as recommendation engines

The goal of this presentation and associated paper is to present results of investigation related to use of the Extreme Gradient …

Human capital and human resources are key success factors for multiple knowledge-based companies. Managing competences of employees is …

H2O AI is one of the most interesting out-of-the-box machine learning tools. It has plenty of algorithms implemented, those algorithms …

Machine learning algorithms are replacement for an old-fashioned advisory systems. How we can utilize them in such a way? How white-box …

Recent Publications

This paper presents a proposition to utilize flexible neural network architecture called Deep Hybrid Collaborative Filtering with …

The goal of this paper was to investigate use of the Extreme Gradient Boosting XGBoost algorithm as a forecasting tool. The data …

Modern decision support systems make use of machine learning and artificial intelligence to solve complicated problems. One of them is …

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