Renee Ahel, Developer in Zagreb, Croatia
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Renee Ahel

Verified Expert  in Engineering

Machine Learning Developer

Location
Zagreb, Croatia
Toptal Member Since
June 18, 2020

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.

Availability

Part-time

Preferred Environment

RStudio, Linux, Windows

The most amazing...

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

Work Experience

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: Amazon API Gateway, AWS Lambda, Apache Spark, Hadoop, RStudio Shiny, DataTables, sparklyr, Purrr, Tibble, Readr, Ggplot2, Dplyr, Tidyverse, R

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: SPSS Modeler, SQL, Apache Hive, Big Data, Cloudera, Oracle SQL, Machine Learning, DataTables, Cron, H20, Ggplot2, R

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: SPSS Modeler, SAS Enterprise Miner, SAS, Oracle SQL

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: Trac, Subversion (SVN), .NET, ComponentOne, Visual Basic .NET (VB.NET), ADOMD.NET, MDX

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: Microsoft Foundation Classes (MFC), Microsoft Foundation Class (MFC) Library, C++, Microsoft Access, SQL Server 2000, .NET, C#

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++, Microsoft Foundation Class (MFC) Library, Microsoft Foundation Classes (MFC), SQL Server 2000, .NET, C#

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: Microsoft Access, Microsoft Foundation Class (MFC) Library, Microsoft Foundation Classes (MFC), C++, Oracle9i, ASP.NET, C#

Insights From Web Shop Sales Data: A Demo Data Science Project

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

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
2004 - 2010

Master of Science Degree in Machine learning

University of Zagreb, Faculty of electrical engineering and computing - Zagreb, Croatia

1998 - 2003

Bachelor of Science Degree in Machine learning

University of Zagreb, Faculty of electrical engineering and computing - Zagreb, Croatia

MARCH 2020 - PRESENT

Data Manipulation with data.table in R

Datacamp

MARCH 2020 - PRESENT

Data Scientist with Python Track

Datacamp

MARCH 2020 - PRESENT

Introduction to Deep Learning in Python

Datacamp

MARCH 2020 - PRESENT

Introduction to Network Analysis in Python

Datacamp

MARCH 2020 - PRESENT

Joining Data with data.table in R

Datacamp

MARCH 2020 - PRESENT

Manipulating Time Series Data with xts and zoo in R

Datacamp

MARCH 2020 - PRESENT

Parallel Programming in R

Datacamp

MARCH 2020 - PRESENT

Python Programmer Track

Datacamp

MARCH 2020 - PRESENT

Supervised Learning with scikit-learn

Datacamp

MARCH 2020 - PRESENT

Time Series with data.table in R

Datacamp

MARCH 2020 - PRESENT

Unsupervised Learning in Python

Datacamp

MARCH 2020 - PRESENT

Writing Efficient R Code

Datacamp

FEBRUARY 2020 - PRESENT

Statistical Thinking in Python (Part 2)

Datacamp

AUGUST 2019 - PRESENT

Interactive Data Visualization with Bokeh

Datacamp

AUGUST 2019 - PRESENT

Introduction to Data Visualization with Python

Datacamp

AUGUST 2019 - PRESENT

Statistical Thinking in Python (Part 1)

Datacamp

MAY 2019 - PRESENT

Introduction to Databases in Python

Datacamp

APRIL 2019 - PRESENT

Manipulating DataFrames with pandas

Datacamp

APRIL 2019 - PRESENT

Merging DataFrames with pandas

Datacamp

APRIL 2019 - PRESENT

pandas Foundations

Datacamp

MARCH 2019 - PRESENT

Cleaning Data in Python

Datacamp

MARCH 2019 - PRESENT

Importing Data in Python (Part 2)

Datacamp

MARCH 2019 - PRESENT

Importing Data in Python (Part 1)

Datacamp

MARCH 2019 - PRESENT

Intermediate Python for Data Science

Datacamp

MARCH 2019 - PRESENT

Introduction to Python

Datacamp

MARCH 2019 - PRESENT

Machine Learning with Tree-Based Models in R

Datacamp

MARCH 2019 - PRESENT

Python Data Science Toolbox (Part 2)

Datacamp

MARCH 2019 - PRESENT

Python Data Science Toolbox (Part 1)

Datacamp

JANUARY 2019 - PRESENT

Conda Essentials Course

Datacamp

DECEMBER 2018 - PRESENT

Introduction to Shell for Data Science

Datacamp

NOVEMBER 2018 - PRESENT

Sequence Models

Coursera

NOVEMBER 2018 - PRESENT

Deep Learning Specialization

Coursera

OCTOBER 2018 - PRESENT

Neural Networks and Deep Learning

Coursera

OCTOBER 2018 - PRESENT

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Coursera

OCTOBER 2018 - PRESENT

Convolutional Neural Networks

Coursera

SEPTEMBER 2018 - PRESENT

Introduction to Spark in R using sparklyr

Datacamp

JUNE 2018 - PRESENT

Building Web Applications in R with Shiny

Datacamp

JANUARY 2017 - PRESENT

Python 3 Tutorial

Sololearn

NOVEMBER 2010 - PRESENT

Predictive Modeling Using Logistic Regression

SAS Institute

SEPTEMBER 2010 - PRESENT

Applied Analytics Using SAS Enterprise Miner 5.3

SAS Institute

MAY 2009 - PRESENT

SAS Enterprise Guide - ANOVA, Regression and Logistic Regression

SAS Institute

APRIL 2009 - PRESENT

SAS Macro Language

SAS Institute

NOVEMBER 2008 - PRESENT

Predictive Modeling Using SAS Enterprise Miner 5.1

SAS Institute

DECEMBER 2005 - PRESENT

Microsoft Certified Application Developer

Microsoft

DECEMBER 2005 - PRESENT

Microsoft Certified Solution Developer

Microsoft

MAY 2005 - PRESENT

Microsoft Certified Professional

Microsoft

Libraries/APIs

Tidyverse, Ggplot2, XGBoost, REST APIs, JSON API, ADOMD.NET, Microsoft Foundation Class (MFC) Library, Pandas, NumPy, Matplotlib, Scikit-learn, Microsoft Foundation Classes (MFC)

Tools

Microsoft Excel, Office 2016, SAS Enterprise Miner, SAS Enterprise Guide, SPSS Modeler, Microsoft Power BI, Google Sheets, Subversion (SVN), Trac, Dplyr, Readr, Tibble, sparklyr, DataTables, Cron, Cloudera, Microsoft Access, Git, GitHub

Languages

R, SQL, SAS, XML, MDX, Visual Basic .NET (VB.NET), C++, C#, Python 3, Python, Bash, Bash Script

Paradigms

DevOps, Data Science, Database Design, Business Intelligence (BI)

Platforms

AWS Lambda, RStudio, Windows, H2O Deep Learning Platform, Amazon EC2, H20, Amazon Web Services (AWS), Linux

Storage

Oracle SQL, Databases, Company Databases, Oracle RDBMS, Database Modeling, JSON, PostgreSQL, Oracle9i, SQL Server 2008, SQL Server 2000, Apache Hive, MySQL

Frameworks

RStudio Shiny, .NET, Hadoop, ASP.NET, Apache Spark, Spark

Other

Data Engineering, Software Development, Machine Learning, Data Mining, Data, Data Analysis, Data Modeling, Documentation, Requirements & Specifications, Writing & Editing, API Documentation, Algorithms, Data Queries, Computational Linguistics, Natural Language Processing (NLP), Regular Expressions, Visualization, Presentations, SAS Macros, Base SAS, Amazon API Gateway, Ghostwriting, APIs, Generative Pre-trained Transformers (GPT), ComponentOne, Purrr, Big Data, Architecture

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