Olha K. Python Engineer, Data Quality, ML

Data QA, QA Automation / Testing

Summary

- 13 years of experience in IT
- Proficiency in Python, Pandas.
- Data quality for ICC profiles and algorithms for display color calibration
- Mathematical modeling (MATLAB, Octave, Comsol, MathCad), software algorithms, numerical methods (algebra, interpolation, regression, nonlinear problems, optimization, ordinary and partial differential equations), machine learning.
- Upper-Intermediate English.
- Available in two weeks

Experience

Data Quality Automation Engineer, Amazon e-Commerce Aggregator

The Amazon aggregator, one of the leaders in buying up and scaling third-party merchants selling on Amazon and other marketplace platforms

February 2021 - Present time

Responsibilities:

  • Analyse and test big volumes of data from different sources: API, Web -scrapers etc.
  • Analyse business requirements and communicate with stakeholders to identify key points.
  • Create test documentation (Test Plans, Test Reports etc.).
  • Manual check for new features, bug fixes, changes.
  • Expand and maintain Data Testing Framework with new automated Python scripts for testing data quality (completeness,  accuracy, uniqueness etc.) and the outcomes of data transformation activities.
  • Prepare Airflow DAGs for scheduled running of automation test tasks.
  • Monitoring tests results.
  • Refactor and support automation tests.
  • Prepare Sisense dashboards for monitoring results of automated tests. 

Technologies:
Python3, SQL, PostgreSQL, Allure Framework, Sisense, AWS (Amazon S3, Amazon Redshift), Snowflake, AirFlow, Git, GitHub, Jira, CircleCI

Maternity Leave

2019-2021

Algorithm Developer, Software Technologies Inc.

April 2011 - October 2018 

Description: Medical imaging, photo, geospatial industries, Automotive. 
Responsibilities:

  • Data Validation for Product Management Department
  • Development of algorithms for professional color calibration and profiling software (QUBYX PerfectLum, QUBYX PressProof and other OEM products):
    - improvement of algorithms for display calibration;
    - development of methodologies and algorithms for creation of CLUT-based  ICC profiles for display, printer (CMYK, RGB, monochrome), camera, scanner; 
    - algorithm development for other helper tools (verification of sensor measurements during printer profiling, calculation of devices gamut intersection etc.).
  • Automotive - for in-car display environment for e.solutions (Audi) Color matching within a set of displays 
  • Development of methodology and models for calibration of colorimeter (QUBYX MicroEye Sensor Module).
  • Implementation of all the developed algorithms in MATLAB.
  • Testing of implemented algorithms, performance of experiments.  
  • Cooperation with the team to help embed new algorithms into the product.

Lecturer at National University

September 2006 - June 2013
Responsibilities:

  • Conducting of lectures and practices for courses "Numerical Methods", "Numerical Methods of Mathematical Physics", "Mathematical and Statistical Methods of Social Processes Analysis".
  • Preparation of the lecture materials, helper documents and tasks for practices (4 published manuals).
  • Bachelors of scientific works supervision.

Senior Researcher at National University

January 2007 - December 2009
Responsibilities:

  • Research and development of mathematical models, methods, and algorithms for computer modeling of 2D and 3D boundary value and initial boundary value problems for partial differential equations.
  • Implementation of the application program package for computer modeling of dynamic problems in MATLAB. 
  • Performing numerical experiments and analysis of the obtained results.
  • Writing of monthly and annual reports.

 

Mathematical Modeling and Numerical Methods, Postgraduate
State Technological University
October 2007 - January 2011

Master of Computer Science
National University 
September 2000 - June 2006

Courses, Certificates

Machine Learning, ml-class.org - online course on Coursera created by Stanford University
April 2012 - July 2012

Course included methods of supervised and unsupervised learning: regression, neural networks, support vector machines, clustering etc.