Renee Ahel, Machine Learning Developer in Zagreb, Croatia
Renee Ahel

Machine Learning Developer in Zagreb, Croatia

Member since June 24, 2018
Renee is a data scientist with over 12 years of experience, and five years as a full-stack software engineer. For over 12 years, he has worked in international environments, with English or German as a working language. This includes four years working remotely for German and Austrian client companies and nine months working remotely as a member of the Deutsche Telekom international analytics team.
Renee is now available for hire

Portfolio

Experience

Location

Zagreb, Croatia

Availability

Full-time

Preferred Environment

Windows/Linux, RStudio

The most amazing...

...prediction model I've built is the one predicting the likelihood that a telecom customer is also using competitor services.

Employment

  • Freelance Data Scientist

    2018 - PRESENT
    Freelance Data Scientist
    • Gathered and presented data from coffee shop registers and the derived customer behavior patterns to enable the marketing team of the beverage producer to make better decisions on how, when, and where to invest the marketing budget.
    • Developed a suite of spend classification models using R language (data.table, ggplot2, xgboost packages), NLP techniques and XGBoost classifier, used AWS Lambda and AWS API Gateway for production deployment.
    • Designed an expert system to enable the client to deliver expert procurement knowledge on creating procurement strategies for his customers.
    • Wrote extensive documentation of the expert system solution to serve as a basis for patent application.
    • Developed a reporting database based on PostgreSQL, using Power BI as frontend. Implemented a data pipeline using R language (tidyverse, jsonlite, httr packages) to integrate with clients Square and Brushfire accounts using Square and Brushfire APIs. The PowerBI dashboards covered business sales, inventory and labor business areas.
    • Authored a technical whitepaper on an edge-based machine learning solution for a client.
    • Delivered a "Data visualization 101" workshop on multiple IT conferences and meetups. The workshop focused on basic data visualization principles - from how human visual cognition works, to basic data visualization forms and most frequent mistakes. There was also an emphasis on creating effective dashboards.
    Technologies: R language, Tidyverse, Dplyr, Ggplot2, Readr, Tibble, Tidyr, Purrr, Sparklyr, Data.Table, Plumbr R Shiny web application framework, Apache Hadoop, Apache Spark, AWS Lambda, AWS API Gateway
  • Data Scientist

    2017 - 2018
    Hrvatske telekomunikacije inc., Zagreb, Croatia – part of Deutsche Telekom
    • Served as a member of an international analytics team of Deutsche Telekom, working remotely from Croatia, with the team manager in Germany. I've used Oracle SQL on the Oracle 12c data warehouse as a data source.
    • Fixed lines churn prediction model enabled early detection of customers with potential to terminate the service, enabling preventive retention actions. I've used Oracle SQL on the Oracle 12c data warehouse as a data source and SPSS Modeler for modeling and deployment to production.
    • Improved households detection significantly increased the potential base of customer households, necessary for offering the companies' flagship product. I've used Oracle SQL on the Oracle 12c data warehouse and Hive SQL on a Cloudera big data platform as a data source, H2O for modeling and R (data.table, H2O, cronR, ggplot2 packages) for additional data preparation, deployment to production and monitoring.
    • Developed propensity models for key products significantly increased the conversion rate. I've used Oracle SQL on the Oracle 12c data warehouse and Hive SQL on a Cloudera big data platform as a data source, H2O for modeling and R (data.table, H2O, cronR, ggplot2 packages) for additional data preparation, deployment to production and monitoring.
    Technologies: R (data.table, H2O, cronR, ggplot2 packages), H2O machine learning library, Oracle SQL, Cloudera Hive big data platform, Hive SQL, SPSS Modeler
  • Data Scientist

    2008 - 2017
    Vipnet LLC, Zagreb, Croatia – part of América Móvil
    • Built a recommender engine generating individualized product suggestions for each business customer, by combining internal and third-party data on business customers. I've used Oracle SQL on an Oracle 12c data warehouse as a data source.
    • Estimated the potential for fixed network expansion with pinpoint accuracy on individual address level for the entire territory of Croatia by combining public and internal company data. It enabled optimal allocation of investment in the fixed network – to areas with the most commercial potential, and lowest construction costs. I've used Oracle SQL on an Oracle 12c data warehouse as a data source.
    • Trained a model estimating the likelihood a customer owns a competitor subscription by combining market research data with internal data. It provided a potential base for cross-sell/up-sell activities. I've used Oracle SQL on an Oracle 12c data warehouse as a data source, SAS for data preparation and deployment to production, SAS Enterprise Miner for modeling.
    • Analyzed customer recharge behavior by creating a recharge based segmentation. The segmentation enabled introduction of new voucher denominations more suited to customer needs. I've used Oracle SQL on an Oracle 12c data warehouse as a data source, SAS for data preparation and deployment to production, SAS Enterprise Miner for modeling.
    • Developed a model predicting which customers are most likely to buy data options. It enabled optimal customer targeting when offering data options. I've used Oracle SQL on an Oracle 12c data warehouse as a data source, SAS for data preparation and deployment to production, SAS Enterprise Miner for modeling.
    • Analyzed the purchase behavior of small businesses by applying market basket analysis to purchase transaction data. It provided new insights usable by sales. I've used Oracle SQL on an Oracle 11g data warehouse as a data source, SAS for data preparation and deployment to production, SAS Enterprise Miner for modeling.
    • Created models predicting churn for the small business segment. They enabled early detection of customers with potential to terminate the service, enabling preventive retention actions. I've used Oracle SQL on an Oracle 12c data warehouse as a data source, SAS for data preparation and deployment to production, SAS Enterprise Miner for modeling.
    • Collaborated with the data warehouse team to redesign the data science data mart. We were engaged in the definition of data sources, data transformations, and database table formats. Following the implementation, we did intensive data quality testing. The resulting data mart was much more suited to our needs and had traceable data sources, which helped to quickly resolve data quality issues. I've used Oracle SQL on an Oracle 11g data warehouse as a data source.
    • During the DWH redesign project, I recognized the business need for a unique customer data set. I've compiled a detailed specification containing complex rules on data processing and data quality improvements. In the process, I profiled two relevant source systems which contained customer data. The resulting unique customer data set is used for company-wide reporting, CRM campaigning and has enabled a tenure-based customer loyalty program. I've used Oracle SQL on an Oracle 11g data warehouse staging area as a data source.
    • Implemented an e-bill affinity prediction model, which predicted which residential customers are most likely to switch to e-bills. It enabled the billing department to speed up the adoption of e-bills. I've used Oracle SQL on an Oracle 11g data warehouse as a data source, SAS for data preparation and deployment to production, SAS Enterprise Miner for modeling.
    Technologies: Oracle SQL, SAS, SAS Enterprise Miner, SPSS Modeler
  • Business Intelligence Developer

    2007 - 2008
    SoftPro Tetral LLC, Zagreb, Croatia
    • Contributed to development work on CubePlayer application, an OLAP client for Analysis Services 2000/2005 using VB.NET 2.0, MDX and ComponentOne for .NET 2.0.
    • Introduced ClickOnce deployment, Subversion source control and Trac issue tracker into the CubePlayer development project.
    Technologies: MDX, ADOMD.NET, VB.NET 2.0, ComponentOne for .NET 2.0, Subversion, Trac
  • Team Lead

    2007 - 2007
    Ekobit LLC, Zagreb, Croatia
    • Developed Taxman, a tax return application targeted to the German consumer market and developed for a German client company Lexware GmbH. I've used C# 2.0, NET Framework 2.0, SQL Server 2000, MS Access 2000 and C++/MFC.
    • Lead a team working remotely on full stack development of Taxman.
    Technologies: C# 2.0, NET Framework 2.0, SQL Server 2000, MS Access 2000, C++/MFC
  • Software engineer

    2004 - 2007
    Ekobit LLC, Zagreb, Croatia
    • Developed MAWIS, an ERP system used in the waste disposal industry developed for a German client, MOBA AG. Work involved maintenance and implementation of new functionality. I've used C++/MFC and SQL Server 2000.
    • Built MAWIS-online, a lightweight web-frontend for the MAWIS ERP system using C# 2.0, .NET Framework 2.0, SQL Server 2000.
    • Created MAWIS.NET, a framework for import/export of data to/from MAWIS ERP system using C# 2.0, .NET Framework 2.0 and SQL Server 2000.
    • Worked remotely on all above mentioned software development projects.
    Technologies: C# 2.0, .NET Framework 2.0, SQL Server 2000, C++/MFC
  • Software engineer

    2002 - 2004
    Okit LLC, Zagreb, Croatia
    • Developed ZAD3-online, a web application used for registration and tracking of failures in the low-voltage power grid developed for a Croatian power utility company using C# 1.0, ASP.NET 1.1 and Oracle 9i.
    • Built ZAD3, a Windows application used for registration and tracking of failures in the low-voltage power grid developed for Croatian power utility company, using C++/MFC and MS Access 2000.
    • Programmed ZAD1, a Windows application used for registration and tracking of failures in the high-voltage and medium-voltage power grids developed for Croatian power company using C++/MFC, MS Access 2000 and Oracle 8i.
    Technologies: C# 1.0, ASP.NET 1.1, Oracle 9i, C++/MFC, MS Access 2000

Experience

  • Insights From Webshop Sales Data - a Demo Data Science Project (Development)
    https://github.com/reneeahel/online-retail-data-analysis

    The purpose of the analysis is to extract insights with business value for a webshop selling all-occasion gifts.

    A large part of the analysis consists of data cleaning and basic exploratory analysis, as usually is the case with data science projects. After those basic steps, I employ machine learning algorithms on Spark to uncover more complex customer behavior patterns, like which products are frequently purchased together.

    Project deliverables are publicly available data science notebook:
    http://rpubs.com/reneeahel/OnlineRetailAnalysisDemo
    and an interactive web application:
    https://renee-ahel.shinyapps.io/OnlineRetailDemo/
    aimed at bringing the project results quickly to the business users.

    Technologies used: R, tidyverse, sparklyr, and Spark.

  • Automatic Key Phrase Extraction System (Development)
    https://www.researchgate.net/publication/242642966_Automatic_Keyphrase_Extraction_from_Croatian_Newspaper_Articles

    As my Master's thesis, I've built a system for automated keyword extraction from Croatian newspaper articles for the Croatian News Agency (HINA). The system learned from articles which already had keywords assigned by humans, and applied that knowledge to assign keywords to new articles. The system was able to handle over 300 million records while running on a laptop. I've published a scientific paper describing the system and discussing the performance.

    Technologies and languages used: SQL Server

Skills

  • Languages

    R, SQL, SAS, XML, C#, Python 3, Python, Bash, Bash Script
  • Libraries/APIs

    Tidyverse, Ggplot2, XGBoost, REST APIs, JSON API, Pandas, NumPy, Matplotlib, Scikit-learn
  • Tools

    R Studio, Microsoft Excel, Office 2016, SAS Enterprise Miner, SAS Enterprise Guide, SPSS Modeler, Microsoft Power BI, Google Sheets, Git, GitHub
  • Paradigms

    Data Science, Database Design, Business Intelligence (BI)
  • Platforms

    AWS Lambda, RStudio, Windows, H2O Deep Learning Platform, AWS EC2, Linux
  • Storage

    Oracle SQL, Databases, Company Databases, Oracle DBMS, Database Modeling, JSON, PostgreSQL, SQL Server 2008, SQL Server 2000, Apache Hive, MySQL
  • Other

    Data Engineer, Machine Learning, Data Mining, Data, Data Analysis, Data Modeling, Data Analyst, Documentation, Requirements & Specifications, English, Technical Writing, API Documentation, Specifications, Algorithms, Data Queries, Computational Linguistics, Natural Language Processing (NLP), Regular Expressions, Visualization, Presentations, SAS Macros, Base SAS, AWS API Gateway, Ghostwriting, APIs, Big Data, AWS, Bash Scripting, Architecture
  • Frameworks

    RStudio Shiny, Apache Spark

Education

  • Master of Science degree in Machine learning
    2004 - 2010
    University of Zagreb, Faculty of electrical engineering and computing - Zagreb, Croatia
  • Bachelor of Science degree in Machine learning
    1998 - 2003
    University of Zagreb, Faculty of electrical engineering and computing - Zagreb, Croatia

Certifications

  • Data Manipulation with data.table in R
    MARCH 2020 - PRESENT
    Datacamp
  • Data Scientist with Python Track
    MARCH 2020 - PRESENT
    Datacamp
  • Introduction to Deep Learning in Python
    MARCH 2020 - PRESENT
    Datacamp
  • Introduction to Network Analysis in Python
    MARCH 2020 - PRESENT
    Datacamp
  • Joining Data with data.table in R
    MARCH 2020 - PRESENT
    Datacamp
  • Manipulating Time Series Data with xts and zoo in R
    MARCH 2020 - PRESENT
    Datacamp
  • Parallel Programming in R
    MARCH 2020 - PRESENT
    Datacamp
  • Python Programmer Track
    MARCH 2020 - PRESENT
    Datacamp
  • Supervised Learning with scikit-learn
    MARCH 2020 - PRESENT
    Datacamp
  • Time Series with data.table in R
    MARCH 2020 - PRESENT
    Datacamp
  • Unsupervised Learning in Python
    MARCH 2020 - PRESENT
    Datacamp
  • Writing Efficient R Code
    MARCH 2020 - PRESENT
    Datacamp
  • Statistical Thinking in Python (Part 2)
    FEBRUARY 2020 - PRESENT
    Datacamp
  • Interactive Data Visualization with Bokeh
    AUGUST 2019 - PRESENT
    Datacamp
  • Introduction to Data Visualization with Python
    AUGUST 2019 - PRESENT
    Datacamp
  • Statistical Thinking in Python (Part 1)
    AUGUST 2019 - PRESENT
    Datacamp
  • Introduction to Databases in Python
    MAY 2019 - PRESENT
    Datacamp
  • Manipulating DataFrames with pandas
    APRIL 2019 - PRESENT
    Datacamp
  • Merging DataFrames with pandas
    APRIL 2019 - PRESENT
    Datacamp
  • pandas Foundations
    APRIL 2019 - PRESENT
    Datacamp
  • Cleaning Data in Python
    MARCH 2019 - PRESENT
    Datacamp
  • Importing Data in Python (Part 2)
    MARCH 2019 - PRESENT
    Datacamp
  • Importing Data in Python (Part 1)
    MARCH 2019 - PRESENT
    Datacamp
  • Intermediate Python for Data Science
    MARCH 2019 - PRESENT
    Datacamp
  • Introduction to Python
    MARCH 2019 - PRESENT
    Datacamp
  • Machine Learning with Tree-Based Models in R
    MARCH 2019 - PRESENT
    Datacamp
  • Python Data Science Toolbox (Part 2)
    MARCH 2019 - PRESENT
    Datacamp
  • Python Data Science Toolbox (Part 1)
    MARCH 2019 - PRESENT
    Datacamp
  • Conda Essentials Course
    JANUARY 2019 - PRESENT
    Datacamp
  • Introduction to Shell for Data Science
    DECEMBER 2018 - PRESENT
    Datacamp
  • Sequence Models
    NOVEMBER 2018 - PRESENT
    Coursera
  • Deep Learning Specialization
    NOVEMBER 2018 - PRESENT
    Coursera
  • Neural Networks and Deep Learning
    OCTOBER 2018 - PRESENT
    Coursera
  • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
    OCTOBER 2018 - PRESENT
    Coursera
  • Convolutional Neural Networks
    OCTOBER 2018 - PRESENT
    Coursera
  • Introduction to Spark in R using sparklyr
    SEPTEMBER 2018 - PRESENT
    Datacamp
  • Building Web Applications in R with Shiny
    JUNE 2018 - PRESENT
    Datacamp
  • Python 3 Tutorial
    JANUARY 2017 - PRESENT
    Sololearn
  • Predictive Modeling Using Logistic Regression
    NOVEMBER 2010 - PRESENT
    SAS Institute
  • Applied Analytics Using SAS Enterprise Miner 5.3
    SEPTEMBER 2010 - PRESENT
    SAS Institute
  • SAS Enterprise Guide - ANOVA, Regression and Logistic Regression
    MAY 2009 - PRESENT
    SAS Institute
  • SAS Macro Language
    APRIL 2009 - PRESENT
    SAS Institute
  • Predictive Modeling Using SAS Enterprise Miner 5.1
    NOVEMBER 2008 - PRESENT
    SAS Institute
  • Microsoft Certified Application Developer
    DECEMBER 2005 - PRESENT
    Microsoft
  • Microsoft Certified Solution Developer
    DECEMBER 2005 - PRESENT
    Microsoft
  • Microsoft Certified Professional
    MAY 2005 - PRESENT
    Microsoft

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