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PIM PHILIPPO
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My journey so far

Vijzelaar Financial Data Analytics Consultant

Vijzelaar.com

Financial Data Analytics Consultant

Oct 2019 - Current


Vijzelaar Financial Data Analytics Consultant

KLM Data Analyst

KLM

Data Analyst - Advanced Data Analytics

Dec 2017 - Sep 2019

As a Data Analyst at KLM I worked closely together with Data Scientists and Data Consultants in a dedicated (Agile) Product Team. As a team we mainly focused on finance related subjects. For example: EU claims (EU261 claims), fraud detection and determining quotes for KLM Engineering & Maintenance (part of the predictive maintenance program).

Focus areas:

  • (Big) Data Analysis
  • Building Data Models
  • Machine Learning
  • Process Mining Analysis

Find out more about the projects I have done at KLM:


In the period December 2017 - April 2018 I worked together with two Data Scientists from GoDataDriven. We worked together on several projects, for example the project "EU Claims Forecast Model". See the casestudy Mastering Financial Information for more information on the collaboration between KLM and GoDataDriven.

KLM Data Analyst

Transavia Business Controller

Transavia

Business Controller IT & Corporate

Aug 2015 - Nov 2017

As a Business Controller at Transavia I focussed on the Overhead Departments and the Innovation Portfolio.

Main activities:

  • Directly reporting to the management group (i.e. CEO, CFO and VP's)
  • Planning and Control cycle (forecasting, budgeting, monthly closing)
  • Monitoring the (IT) innovation portfolio (Transavia Agile Framework, business cases, Capex/Opex, linking the innovation portfolio to the OGSM model framework)
  • Business and data analysis (e.g. ground staff CLA what-if analysis)
  • Creating interactive Power BI dashboards (e.g. route profitability dashboard, management dashboard, finance dashboard)
  • Business improvement (proces optimilization, Lean)
  • (Redesign) Transavia Control Framework (internal control framework)

In addition to the regular activities, I also worked on several projects. Find out more about these projects:

Transavia Business Controller


Erasmus University Rotterdam Finance

Erasmus University Rotterdam

Master of Science (MSc), Accounting, Auditing and Control

2013 - 2015

  • Specialization in Accounting and Finance
  • Graduated in May 2015
Erasmus University Rotterdam Finance


The Hague University Finance

The Hague University of Applied Sciences

Bachelor of Business Administration (BBA), Business Economics

2009 - 2013

  • In addition to obtaining my Bachelor of Business Administration (BBA) in Business Economics, I also obtained a certificate for the academic minor of the Erasmus University Rotterdam in July 2013
  • Graduated in July 2013

Find out more about some activities I have done during my study Business Economics:

The Hague University Finance


Skills

Languages

Dutch


English

Professional skills

  • Data Analysis
  • Business Intelligence
  • Business Analytics
  • Data Visualization
  • Business Control
  • Financial Analysis
  • Data Modeling
  • Machine Learning
  • Process Mining Analysis

Computer

DATA VISUALISATIONS

Power BI
Power BI
Microsoft Office
Spotfire
Microsoft Office
Matplotlib
Dash by Plotly
Dash by Plotly
Microsoft Office
MS Excel
Microsoft Office
MS PowerPoint


DATA ANALYTICS / DATA SCIENCE

Python
Python
Spark
Spark
SQL
SQL
Jupyter Notebook
Jupyter Notebook
Jupyter Notebook
MS Excel
Microsoft Azure
MS Azure
Microsoft SQL Server Management Studio
MS SSMS


WEB DEVELOPMENT

HTML
HTML
CSS
CSS
JavaScript
JavaScript
JQuery
JQuery
Flask
Flask


OTHER

SharePoint
SharePoint
C#
C#


Courses

DataCamp

DataCamp

Supervised Learning with scikit-learn

2019

  • Classification
  • Regression
  • Precision, recall, ROC curve and AUC
  • Hyperparameter tuning (GridSearchCV and RandomizedSearchCV)
  • Preprocessing data for Machine Learning
  • Machine Learning pipelines
DataCamp

Supervised Learning Scikit-learn Classification Regression Hyperparameter Tuning Preprocessing Data ML Pipelines

DataCamp

DataCamp

Importing Data in Python

2019

  • Importing file types such as pickled files, Excel spreadsheets, SAS files, Stata files, HDF5 files and MATLAB files
  • Working with relational databases in Python
  • Importing data from the Internet
  • Interacting with APIs to import data from the web
  • Diving deep into the Twitter API
DataCamp

Python SQL API Twitter API

DataCamp

DataCamp

Python Data Science Toolbox

2019

  • Custom functions (multiple parameters, multiple return values, default arguments and variable-length arguments)
  • Scoping in Python
  • Lambda functions
  • Error-handling
  • Iterators and iterables
  • List comprehensions and generators
DataCamp

Python Data Science

DataCamp

DataCamp

Intermediate Python for Data Science

2018

  • Visualize data with matplotlib's functions
  • Data structures (dictionaries, Pandas DataFrame)
  • Boolean logic, control flow and loops in Python

Python Data Science

Microsoft Virtual Academy

Microsoft Virtual Academy

Introduction to Programming with Python

2017

  • Basic syntax (e.g. data types, variables)
  • Working with dates and times
  • Decision statements (if/else and and/or statements and nested if statements)
  • Iteration statements (e.g. for loop, while loop)
  • Lists, dictionaries and tuples
  • Handling errors

Python Programming Language

Microsoft Virtual Academy

Microsoft Virtual Academy

C# Fundamentals for Absolute Beginners

2016

  • Working with code files, projects and solutions (Microsoft Visual Studio)
  • Decision statements (if/else and and/or statements and nested if statements)
  • Iteration statements, enumerations and the switch decision statements
  • Understanding arrays
  • Defining and calling methods
  • Understanding and working with classes
  • Understanding namespaces and working with the .NET Class Library
  • Creating and adding references to assemblies
  • Working with lists and dictionaries
  • Working with LINQ
  • Handling errors

C# Programming Language Visual Studio

Microsoft Virtual Academy

Microsoft Virtual Academy

From Data to Insight and Impact - The BI Revolution

2015

  • Spend less time struggling with data and more time driving insight and impact
  • Getting any type of data
  • Shaping any type of data
  • Turning your data into a visual story
  • Data exploration for everyone

Finance Business Intelligence Finance at Microsoft

Microsoft Virtual Academy

Microsoft Virtual Academy

Reimagine Finance

2015

The course consists of two parts:

Managing Controls and Compliance

  • The evolution of Controls & Compliance at Microsoft
  • Controls & Compliance tools in action

Finance BI for Sales Organizations

  • The evolving role of Finance at Microsoft
  • Transformational technology in action
  • Understand the (on-premises and cloud) solutions that support this transformation

Finance Controls & Compliance


Hobbies

Programming

In my spare time I like to develop (small) apps or automate things (e.g. Smart Home).

Cycle Racing

I have a Pinarello Marvel and like to do High Intensity Interval Trainings (HIIT).

Reading

I like to read both thrillers and books about programming and Data Science.

Data Science competitions

Recently I have started to participate in Data Science competitions.



Contact

Would you like to contact me?

I promise to answer you as soon as possible.


You can also contact me via LinkedIn.




KLM

EU261 Claim Provisions Compliant with IFRS 15 (Service Recovery Cost)

Dec 2017 - Mar 2018

Insufficient insights in cost drivers regarding EU261 claims make it difficult to create an accurate financial forecast and provision for these cost, and to match the actual cost with this provision. This leaves both KLM and Air France with compliancy issues regarding IFRS 15 in 2018. We created a financial forecast model based on a custom Machine Learning algorithm.


Programming language(s): Python

Dataset size: 0-25GB

Keywords: Machine Learning (ML) Spark Pandas Scikit-learn JSON Robotic Process Automation (RPA)


KLM

External Spend Process Mining Analysis

Mar 2018 - Apr 2018

Together with Celonis we performed a Process Mining Analysis for KLM Inflight Services. Based on this analysis, we assessed the process efficiency and provided insights regarding the Purchase to Pay (P2P) process within KLM Inflight Services. These insights can lead to better and more efficient purchasing behavior.


Programming language(s): Python

Dataset size: 25-50GB

Keywords: Process Mining Pandas Celonis P2P


KLM

Payments Online

Apr 2018 - Nov 2018

There is a continuous trade-off between making a payment method (e.g. Credit Card, iDeal, PayPal) available for the customer and costs incurred as a result of making these payment methods available. Using various advanced data analytics / data science techniques, we have focussed on:

  • The performance of payment methods (e.g. how often do problems with a certain payment method occur?).
  • Calculating the net costs of making a payment method available.
  • Payment method fraud (prevent "review cases" and "reversed payments").

Programming language(s): Python

Dataset size: 50-100GB

Keywords: Spark Pandas Machine Learning Scikit-learn Spotfire


KLM

E&M Time Registration

Jun 2018 - Jul 2018

The registered hours within Maintenix (time registration system) indicate that the quality of registered hours is not at a desired level. This results in a challenge from a utilization point of view (e.g. over- or under-utilization of available capacity). Using various data analytics techniques we provided insights in the hour registration behavior. These insights can lead to better workload planning, FTE forecasts and revenue management.


Programming language(s): Python

Dataset size: 25-50GB

Keywords: Spark Pandas Machine Learning Scikit-learn Spotfire


KLM

E&M Quotes

Jun 2018 - Nov 2018

Whenever a (potential) customer sends out a tender for the maintenance of 737 components, the Cost Quotation team within E&M Component Services has a big role in responding to it with a reliable estimate of future cost so that Sales can create a competitive yet profitable proposal. We optimized and automated the calculation of the cost per shop visit (used for cost quotations). The PoC (Proof of Concept) consisted of the following topics:

  • Using Machine Learning to improve the calculating of future cost (which can have a significant impact on the realized margin).
  • Show, using Python Flask, a possible UI (User Interface) where users can select partnumbers and peer groups (i.e. other airlines) and filter relevant items (e.g. book years).
  • Create a pipeline using Object Oriented Programming (OOP):
    • Ingest (import and clean several datasets)
    • Calibrate (create a model)
    • Predict (make a prediction based on the model)

Programming language(s): Python

Dataset size: 0-25GB

Keywords: Machine Learning Pandas OOP Scikit-learn Flask


KLM

Declaree

Feb 2019 - Mei 2019

The project consists of three tracks:

  • Process Mining Analysis
  • MI Dashboard
  • Process Automation

Programming language(s): Python

Dataset size: 0-25GB

Keywords: DISCO Pandas Machine Learning Scikit-learn Spotfire Pipeline Unittesting


KLM

Flawed Payments

Jun 2019 - Aug 2019

On a daily basis KLM has to make a lot of external payments. Within the payment process there are various checks and balances implemented (both in systems and the process itself) to make sure KLM does not pay invoices twice or pay “flawed” payments. In the past external parties have been hired to test and analyze this process.

To improve the internal control framework and continuous monitor the payment process we implemented two data models. One model to capture duplicate payments based on exact matching and fuzzy matching and one model to determine anomalies (based on an anomaly detection algorithm).


Programming language(s): Python

Dataset size: 0-25GB

Keywords: Flawed Payments Double Payments Data Preprocessing Pipeline Fuzzy Matching Internal Control Anomaly Detection


KLM

Fuel Controlling

Aug 2019 - Sep 2019

A large part of all costs within KLM are related to the purchase and distribution of (jet) fuel. These cost are determined by the volume and the price. The Fuel Controlling department within KLM is primarily responsible for the controlling of the purchase and distribution of fuel and the hedging of the related costs. A couple of time intensive internal controls take place related to this process.

To get familiar with the different data sources and processes we have automated some of these time intensive internal controls. Among others, for this, a data preprocessing pipeline has been established.

After the proof of concept phase, we created a webservice so that the business (Fuel Controlling department) can access and run the automated internal control themselves. The webservice is created in Flask (Python) and deployed in Kubernetes (in a Docker container).


Programming language(s): Python

Dataset size: 0-25GB

Keywords: Robotic Process Automation (RPA) Webservice Flask Kubernetes Docker


TRANSAVIA

Airsuite

Mar 2016 - Feb 2017

Within the project "Airsuite", we built a modern cloud-based solution for airline financial management, route profitability and purchasing. This was done together with Transavia France, Capgemini and the Solmate Group. The project constists of a mix of three platforms:

  • NetSuite
  • Airpas
  • Adaptive Insights

Some important items that I have helped with are:

  • Creating a new Chart of Accounts (COA).
  • Create an initial BI 'layer' structure and align this new BI structure with Transavia France.
  • Testing the functionality of NetSuite and Adaptive Insights.
  • Brainstorming about creating custom solutions within NetSuite and Adaptive Insights.

Go live date: January 2017

Keywords: NetSuite Airpas Adaptive Insights Transavia France Capgemini Solmate Group


TRANSAVIA

New Digital Workplace

2016 - 2017

Within the project "New Digital Workspace" I signed up as an early adopter of the new Transavia (digital) workplace. For this project I, amongst others, tested several programs in the new workspace and created the whole SharePoint Online (SPO) environment for Transavia Finance (Accounting and Controlling & Reporting) and several other departments/teams within Transavia (e.g. HR, scrum teams, change & innovate). For Controlling & Reporting specifically I, amongst others, set up the following:

  • Teamsite
  • Document libraries
  • Permission levels
  • Dashboards
  • Online forms

Pogramming language(s): JavaScript HTML/CSS

Keywords: SharePoint Teamsites Digital Workspace


TRANSAVIA

Proof of Concept Power BI within the Finance department

Nov 2016 - Mrt 2017

Together with a Financial Controller I set up two Proof of Concepts (POCs) in which we proved the value of Power BI for Transavia in general and Transavia Finance in particular. These dashboards with different levels of details have been demonstrated to and discussed with the Board of Directors (including managers of different departments) in periodic meetings. These dashboards are currently still being used. The two dashboards explained:

  • Overall dashboard - A large dashboard consisting of several reports with the most important Transavia KPIs within the fields of:
    • Customer
    • Operational performance
    • Corporate / HR
    • Innovation
    • Financial Performance
  • Route Profitability - A dashboard with an image of Europe. Per country/route the Route Profitability (COI) can be seen over different time periods (MoM, YoY) and versions (actual, forecast and budget).

Currently Power BI is used in a larger part of the organization.


Keywords: Power BI SharePoint Dashboards


THE HAGUE UNIVERSITY OF APPLIED SCIENCES

Give tutoring to first-year Business Economics students

Sep 2010 - Jun 2011

Activities:

  • Teach in the first year Business Economics subjects (e.g. Financial Accounting, External Reporting, Cost Accounting, Microsoft Excel)
  • Help students to improve their study approach
  • Support student with learning goals

Certificate: Tutoring certificate obtained in June 2011


THE HAGUE UNIVERSITY OF APPLIED SCIENCES

Communication Training in the French Ardennes

2010

Training in:

  • Individual presentation
  • Leading a group
  • Learn to obtain information through open and closed questions
  • Learn to give and receive feedback