lang-uk/recruitment-dataset-candidate-profiles-english huggingface dataset:
Position: Data Scientist / Data Analyst
Category: Data Science
Experience: 3 years | English: pre

Dashboards developing (Shiny, flexdashboard);
Preparing reports;
Managing the data-based projects
As Freelanced Data Scientist on Upwork (4 years): R Script Development, Statistical analysis, MySQL, Shiny App Development.
Previously I have been working as data analyst in product company for a bit more than 10 year. Main tasks - Analysis of markets, competitors, marketing activities (events, sales promotion etc.)
Sales statistics; Web-analytics and it impact on sales.
Participation in developing marketing strategies and opportunities on new markets (market size, key players, trends etc.).
Responsible of communications with analytics agencies.
Responsible for acquiring new customers from external sources.
Responsible for conducting surveys of customers.
Professional growth Challenging tasks Awesome team
Position: Data Scientist / Data Analyst
Category: Data Science
Experience: 3 years | English: intermediate


Data analysis and preprocessing, feature selection. Implementation ML models and comparing the results, improving the quality of existing models.
Expect interesting and complex tasks that will drive and keep you focused.
Position: Data Scientist / Data Analyst
Category: Data Science
Experience: 5 years | English: upper


ETL pipeline for automatic onboarding and preprocessing of different "products" of new "customers" on the B2B platform (AWS Glue/Lambda/Step function + PySpark + PostgreSQL, business details NDA, automotive niche)

Optimization and refactoring of PySpark ETL jobs

A/B Test Framework (Python modules + Jupyter, tools for ab testing specially made for product needs)

Spam/Scum Filter (Flask + scikit-learn (bayesian + logreg) + specific language preprocessing for product with slavic location)

Marketing ROI Prediction (Tableau Online + Big Query + Python ETL + simple prediction model)

Full Reporting (Tableau Online + BigQuery, Python scripts that generate PDFs for C-level mail, Grafana)

Product/Marketing Metric Alerting System (Python scripts with models + Slack)

Auto Photo Moderation (Automation decline of photo if there are no person or more than 1 person, YOLO + Flask)

Addition skills: 200+ A/B tests, different calculators/alertings/tools in Tableau/Python/GoogleSpreadSheets, product metric modeling, user segmentation, product analytics/managment

Domain: Online Dating, Digital Marketing, FinTech, Mechanical Engineering

I want to work with the interesting product and solid team that can give me opportunity to improve my engineering skills (ML/DE). In case of Product Analyst role, I would like to work with the product that I could use myself for better immersion.
I dont want to work with grey/black niches (casino, betting, hook up, scum, etc)
Position: Data Scientist / Data Analyst
Category: Data Analyst
Experience: 1 years | English: pre


Experience:
QSS Analytics - Data Science Intern
Data Science Academy - Python Mentor
Osmanbeyli Academy - Instructor (Excel for Data Analysis)
The Forage - KMPG Data Analysis (Virtual Experence) 


Competitions:
Turkish Airlines Travel Datathon


Projects: (Python, Excel, Power BI)
Walmart Sales Data (2021)
Weekly sales forecasts of Walmart grocery chain were calculated.

Telco Customer Churn (2021)
It has been investigated whether the customers of a telecommunications company will churn the company.

City Development Index (CDI) (2021)
I tried to estimate City Development Indexes

Financial Performance Analysis with TOPSIS and CRITIC methods (2022):
Financial performance analysis of Non-Bank Credit Institutions in Azerbaijan was conducted. TOPSIS and CRITIC methods from Multi-Criteria Decision Making (MCDM) methods were used in the analysis.

Credit Risk Analysis in Python
Position: Data Scientist / Data Analyst
Category: Data Science
Experience: 2.5 years | English: fluent

-Recommendation system, increased click-through rate by > 20%. All the conventional approaches, including collaborative filtering, item-based and hybrid algorithms, were failing in the case of C2C marketplace, where the lifetime of ads is very short. It was impossible to collect statistically significant data about a particular ad or user, so we moved from user-ad interaction granularity to user cluster - ad cluster. I was responsible for the clusterization of users, which was done based on their interactions as clicks, views and contacts with different categories of good. I tried several unsupervised learning algorithms, and Latent Dirichlet Allocation turned out to be the best in terms of business interpretability. This clusterization showed us some interesting segments of users including 'people who just moved to a new place' (were buying different types of furniture, carpets and some housing furniture) or segment of 'students' (looked for language courses, books, visa services).

- Predicting the speed of sales of products and optimizing it by providing a seller with recommendations for an ad design. XGBoost for predicting hours till the sale; using prices, features from the product description and product picture as variables. Afterwards, we used Shap and Lime explainer to provide the seller with factors that slow down the sale process. The model for sell-time prediction is also used as a proxy for ad quality to sort ads in the recommendation block. The accuracy of a model is nearly 80%, and it is already used in production.

- Survival model for churn prediction with 70% accuracy. The model is build to detect those with a high risk of churn, find the early predictors of churn and act to save customers. Still in the experimental phase.

- Find duplicate products based on the photo for the online marketplace. We implemented an algorithm stacking two-level clusterization and Similarity Network (deep learning), and increased the grouping level by ~30%.
I am a Senior Data Science consultant at a European consulting company. Main responsibilities include Data Science team management, project negotiation and project delivery.

I have 2+ years of experience working in the field, proficiency with statistical analysis (hypothesis testing, parameter estimation, confidence intervals, Monte Carlo, regressions), classical ML models (boosting, bagging, SVM etc.) and Deep Learning(CNN, Similarity Net, LSTM, Binary Networks etc). 

Also, I have 1-year of experience of research work in the Deep Learning field in the top European research centre.
I expect either Data Scientist or Data Analyst position in a start-up or a product company. Project/Team management is a possibility as well.
Position: Data Scientist / Data Analyst
Category: Data Analyst
Experience: 10 years | English: upper

PhD, I lived and worked in 4 countries, I have an experience to work in international environments, also, I have an experience of remote work, writing algorithms for developers, presentation skills, managing a team
I am Data Scientist/Data Analyst with PhD, I have broad experience in work for academy and business. I am mainly working with such tools as python/pandas, Tableau, Matlab, KNIME, topcat,  MySQL/ Postgres . I deal with data mining, visualization, statistics, modelling, algorithms, scientific writing & proposal writing (Horizon 2020), raw data processing, machine learning, natural language processing, analytics, sport betting/ football, basketball
Data analysis, descriptive & predictive statistical analyses, statistics & algorithms, machine learning, natural language processing, clustering, regression etc. I am interested in research work & algorithms, work with data, plots, new insights, but not only work with the code ( I am rather researcher/statistician/core algorithm developer than engineer). I have an experience of managing data team.
Position: Data Scientist / Data Analyst
Category: Data Science
Experience: 6 years | English: fluent

• Developed two different approaches of unsupervised machine learning on video game players by utilizing biochemistry methods from DNA sequence mining (CM-SPAM) and algorithms from hierarchical (AGNES), density-based (HDBSCAN) and partitioning (KMeans) families of clustering techniques in R and Python
• Deployed one of approaches above using Spark, Hive and Impala SQL thereby delivering weekly insights to several stakeholders on completely automated basis via Tableau dashboards and various reports.
• Presented other segmentation approach at Machine Learning Innovation Summit 2018 in San Francisco, Data Innovation Summit 2018 in Stockholm and Nordic Games 2018 in Malmö to audiences of 200-600 attendees
- I did a Master of Finance at Massachusetts Institute of Technology in 2016-17 with a very heavy focus in Data Science and Analytics. 
- Between 2017 and 2019, I have worked in Sweden as a Data Scientist at Ubisoft, a large video games publisher, for two years, building an Analytics and DS pipeline for a AAA game from scratch. That included designing tracking events, overseeing implementation, testing and usage for reporting. 
- During my time in Sweden, I built a user segmentation ETL using Python, SQL, PySpark and Hive to cluster video game players using an unsupervised ML model - it was a combination of KMeans and AGNES. I presented my approach on 3 large DS conferences - one in Stockholm, one in San Francisco and one in Malmo. I have also built a recommendation engine to recommend in-game items to players using ALS algorithm. 
- Between 2019 and 2021 I have been the first Data Scientist at a Warner Brothers video games studio in London - I have set up a Data Collection, Analytics and Reporting pipeline using Amazon Redshift, Airflow, Python and Looker from scratch, while also hiring, onboarding and managing a team of 4 analysts. 
- I have also been a DS consultant to a US-based sports analytics startup for 18-month period till it got acquired, where I was part of the team building entire DS pipeline from scratch.
I am looking for a full remote part-time role for 20 hours per week with flexible hours. 
I really appreciate nice people, open communication, openness to feedback and flexibility.
Position: Data Scientist / Data Analyst
Category: Data Science
Experience: 3 years | English: fluent


Led a team on a Video to Text Summarization Project using Hugging face Transformers. (Hugging Face Transformers, AWS S3 Bucket, AWS EC2, , Swagger)
Used MS SQL Server Reporting Services(SSRS) and PowerBI to create, execute, and deliver tabular and charts reports. (SQL, Excel, SSRS, PowerBI)
Designed several machine learning models including Customer Segmentation, Customer Churn, Customer Lifetime Value Models (Pandas, Numpy, Scikit-Learn, Matplotlib, Azure ML Service, AutoML)
Sentiment analysis . (NLTK, NLP, Naive Bayes, Classification, Streamlit)
Worked with a team to build and deployed a prediction model that estimates energy production load shortfall. (Pandas, Numpy, Scikit-Learn, Matplotlib, Streamlit)
Built a Recommender System(Numpy, Pandas, Ntlk, Streamlit)
Position: Data Scientist / Data Analyst
Category: Data Science
Experience: 2.5 years | English: intermediate

Analysed of users behavior of the top media service in East Europe.
Master's degree in Applied Mathematics. 

Passed several DS & ML courses.

Worked as Data Analyst. 
Carried out segmentation of users for mailing / pushing, prepare data for ad-hoc queries, made dashboards and data visualisation, analyzed data using machine learning algorithms for finding insights, outliers detection.
Interesting projects, friendly colleagues, flexible working hours
Position: Data Scientist / Data Analyst
Category: Data Science
Experience: 0.5 years | English: upper

CERTIFICATES
6 certificates of Google Analytics specialization 
The Elements of Data Science by Amazon
Introduction to TensorFlow for Artificial Intelligence, Machine Learning,
and Deep Learning by deep learning.ai
Data Science by Harvard University
Machine learning by the University of Washington

ACHIEVEMENTS
Make good progress in Kaggle competitions
Top 1% in hosing price competition resolving regression problem.
Top 3% in Titanic competition resolving classification problem
PROJECTS
Retail
In the retail industry, I did perform sales analysis to understand patterns and
seasons trends, using that data I made warehouse optimization that
allowed to allocate capital in goods that have more sales potential and that
boosted sales and made capital allocation more effective.
Proprietary trading
Analyzing different trade methodic on different financial instruments did
uncover the common pattern that allowed optimize MTS for making more
profit with less risk (Sharp ratio)
Good team and interesting project
Position: Data Scientist / Data Analyst
Category: Data Science
Experience: 5 years | English: upper


• Short-term inflation forecasting project from scratch based on web-scraped data from online supermarkets: 
- construction of web-scraping system (> 100,000 unique SKU’s daily);
- maintaining and updating the database (SQL, Python);
- classification of the collected data according to a given structure with NLP and NN;
- calculation of forecast indicators for high-frequency time series.
• Investigation the role of the media in formation of inflation expectations using NLP and ML techniques.
• Management and coordination of the R&D team for short-term forecasts of economic indicators based on the novel types of data and techniques.
• Presentation of the results of work within the organization, strengthening international cooperation and improving the reputation of the employer:
- development and holding of an international workshop for representatives of the world's central banks (12 Central Banks, about 100 participants) on the use of Python for macroeconomic research;
- participation in international conferences;
- conducting internal training and research seminars.
Position: Data Scientist || Data Analyst
Category: Data Science
Experience: 3 years | English: fluent

IELTS 7 (reading 9 / 9)
- Performance Analysis 
Prepared Weekly KPI performance Analysis dashboards: KPI list as Parameter, Weekly time plot of each KPI, List of top 5 tariffs for each KPI and growth rate for each of them in the last week, Daily performance of each KPI in the last week, total amount of KPI in the last week, KPI growth in last week. Prepared Daily Revenue performance Analysis dashboard by including: Total revenue, Revenue proportion of each tariff, Compare tariffs on daily basis, visualize cluster of tariffs for the voice and data revenue. Add daily timeline of voice and data revenue for that tariff as tooltip. Portion of data revenue in the total revenue for each tariff.
- Telecommunication Babysitting Business Case with SPSS, Tableau (2022)
I have been given a list of new subscribers in Telecommunications Company. Churn status, revenue, voice and data usage of each subscriber was available in provided sample data. I am asked to support the marketing team to launch data driven babysitting campaign. Using appropriate analysis to explore which variable affect churn most and detect customer clusters with the highest churn rate. Run a campaign to prevent churn and retain subscribers.
- McKinsey Pyhton 
Participant of Prohack: International Data science Hackathon by McKinsey & Company. I was always on the lookout for machine learning projects and competitions; The McKinsey hackathon gave me an opportunity to compete for a new job, which was a nice bonus. Plus, I was curious about the type of problems McKinsey would present. It was a great experience to compete with professionals all over the world and learn from different solutions.
- SPSS & Tableau Project (2022)
I have been given a list of new subscribers in Telecommunications Company. Churn status, revenue, voice and data usage of each subscriber was available in provided sample data. I am asked to support the marketing team to launch data driven babysitting campaign. Using appropriate analyses to explore which variable affect churn most and detect customer clusters with the highest churn rate. Run a campaign to prevent churn and retain subscribers.
Position: Data Scientist, Data Analyst
Category: Data Science
Experience: 3 years | English: pre


06.2016 – 10.2020: project manager, data analyst, Scrum-master in IT-company.
03.2015 – 06.2016: manager in a financial company.
10.2005 – 03.2015: Deputy Chief of Main Statistical Department of Region. 
I worked with data bases of social and economic indicators, with data analysis and presentation of results. 
09.1995 – 06.2007: Assistant and assistant professor in Chernivtsi National University. 
I lectured many mathematics and economics courses (for instance, "Probability Theory and Mathematical Statistics", "Data Analysis"). I have published over a hundred scientific and educational-methodical papers from mathematical and economic subjects, including mathematical programming, probability theory and mathematical statistics.
Position: Data Scientist, Data Analyst
Category: Data Science
Experience: 5 years | English: upper

Ph.D. in Economics
Docent: academic status
14 years in the field of education: econometrics, time series analysis, machine learning, statistics.

Main projects:
1. Production optimization: R, tidyverse
• Conducted production process analysis and identify critical issues and gaps for an established process center
• Identified influential indicators of production optimization and their relationship
• Build predictive models using various machine learning tools to predict product growth

2. Formation and approaches to forecasting the exchange rate: R, tidyverse
• Mentoring a group of researchers, monitoring the timing and quality of implementation.
• Dive the interaction and partnership between the managers to ensure active cooperation in identifying as well as defining analytical needs
• Build predictive models with a variety of machine learning tools to predict currency rates.
• First place in the competition

3. Financial and economic justification of the legislative initiative: R, tidyverse
• Developed a model of clustering of draft laws using SOM for scaling and forming clusters of similarity of projects
• Developed an information processing algorithm with the involvement of NLP and methods of machine and statistical learning
• Led training sessions on the econometrics and clustering algorithms for justification of the legislative initiative

Now I'm working as Associate Professor at Department of Mathematical Modeling and Statistics, ІІТЕ, KNEU.

I want to join the data science team and deepen my knowledge and skills.
I WANT to do:
- work in team
- self-development and self-education
- share and gain knowledge
- time series analysis
- other fields of data science

I do NOT want to do:
- work alone on a project
- routine work without the possibility of development and improvement.
Position: Data Scientist, Data Analyst
Category: Data Science
Experience: 4 years | English: upper

2023: DeepLearning.AI TensorFlow Developer, (Credential ID: 7BVPB567NNCS)

2023: Google Data Analytics Professional Certificate (Credential ID: 5PYV9F68S6N2)

Publications:
2010: "The problem of avoiding collisions with a group of managed objects”, 13th International Conference on behalf of Academician M. Kravchuk.

2010: “Two problems in the non-linear escaping theory with groups of participants”, “Cybernetics and Computing Hardware” (#160, 2010).
1. GlobalFreelance: 05/2010 – until now
Freelance Expert
Writing different learning activities for students worldwide in mathematics, statistics and probability, programming, data analysis, data science, econometrics, project management (via MS Project), quantitative methods, optimization problems, portfolio optimization, etc.
I've worked for more than 4 years mainly with R programming (I have basic knowledge of Python ML tools) tools as a data analyst/data scientist by completing different projects via hypothesis testing and machine learning (using such tools as Regression, Decision, Trees, SVM, SVR, K-NN, Naïve Bayes, etc.), data visualization and presentation of the results via RMarkdown, Tableau.

2. V. M. Glushkov Institute of Cybernetics of NAS of Ukraine:  11/2014 - 01/2020
Junior Researcher at Controlled processes optimization Department #165.
I was working on an escaping problem in n-dimensional space (differential games).
Position: Data Scientist, Data Analyst
Category: Data Science
Experience: 6 years | English: upper

As a co-founder of Equipmetry, a fleet management system, I have leveraged the most effective fleet management methodologies and Machine Learning algorithms to provide companies with a powerful solution for managing their fleets.
The system has helped numerous companies to optimize their fleet operations and reduce costs.


One of our notable achievements has been optimizing spare parts and consumables spends, resulting in a
20% decrease in long-tail spending.
Decreased emergency repairs by 30%
Improved the fleet repair process from 61 days to just 30 days.
Implemented Predictive and Preventive Maintenance methodologies and Job Optimization algorithms, resulting in a
significant decrease in unplanned downtime.


Through Equipmetry, I have demonstrated my ability to leverage cutting-edge technology and Machine Learning, Business
Analysis methodologies to deliver value to companies across industries. I am confident in my ability to continue driving measurable results and improving business outcomes for my clients.

Developed a sophisticated system for forecasting and optimizing schedules for workers in companies with shift-based schedules, using a combination of machine learning and linear programming. I successfully implemented this system at Magnum Cash & Carry LLC, the largest chain of supermarkets in Kazakhstan with approximately 80 locations Decreased spending costs by approximately 25%, resulting in savings of $3 million per year.
Reduced clients' waiting times by approximately 40%, from 30 minutes to just ~15 minutes.
As a Data Scientist, Data Analyst, and Machine Learning Engineer with 6 years of experience, I have a successful history of working across a variety of industries, including manufacturing, industrial, retail, telecommunications, banks, and startups.
My work has focused on leveraging Data Science, Optimization methods, and Data Analytics to drive business outcomes and improve customer experiences.


Developed and implemented ML algorithms that decreased emergency repairs by 30%, resulting in significant cost savings and increased operational efficiency. Utilized optimization methods to improve waiting times for clients, resulting in a 40%
reduction in wait time (from 30 minutes to 16-18 minutes) across 85 departments in hypermarkets.
Position: Data Scientist, Data Analyst
Category: Data Science
Experience: 1 years | English: intermediate

- reporting automation: it allowed to save the analyst’s operating time by 2 times and allowed to receive flexible quick-change reports (pawnshop network)
- building a reporting system and indicators: it made it possible to manage the profitability of the direction and the efficiency of processes in the department (telecommunication company)
Extensive experience in sales analytics, I have experience with big data. Now I'm studying the Data Science and looking for work in this direction
I have good experience in analytics, but now I'm fascinated by data science. So I am looking for a job that will allow me to gain good experience in data science. I want to work in an interesting project
Position: Data Scientist, Data Analyst
Category: Data Science
Experience: 1 years | English: intermediate


Had projects in:
Healthcare (medical insurance)
Retail (classification of goods)
Finance (traders behavior)
NLP (spam classification, texts clustering)
Steganalysis
Object detection

Responsibilities:
Data mining, cleaning and exploration
Data visualization
Feature engineering
Developing, training and testing predictive models
Presentation of the obtained results
I'm looking for interesting projects and highly qualified team, want to develop my skills and work with new technologies.