Ruud is a business-minded Data Scientist with 7+ years of technical expertise in the delivery of valuable data-driven solutions and insights on projects via data analytics and advanced data-driven methods. He provides solutions that drive sustainable corporate growth; with gains in customer loyalty and profits.
Used an NLP-based deep learning model and Seq2Seq to train a chatbot on past customer service conversations and respond to future messages in a way that a real customer service employee would.
The model helped the company deepen relationships with their customers, extended their CLV and gave the customers quicker and more personalized responses. After implementation, the new model boosted customer satisfaction by 25%, reduced average waiting time by 10% and improved the NPS by 7%.
The objective of the project was to develop a churn prediction model that analyzes subscribers’ past behaviour for use in the prediction of the likelihood of churn of any subscriber, for the immediate next month. The base prediction model was created using Logistic regression and the precision of the model was around 57%. Worked on feather enhancements to improve the precision of the overall ensemble model; by including more variables from social network analysis (SNA) based interaction of the subscribers. The overall accuracy of the ensemble was enhanced by another 18% using augmented variables.
The objective of the project was to stabilize or increase retention in the company’s stronghold areas, which were poorly connected neighbourhoods with little to no access to high-speed internet. Developed a web scraping bot constructed to scrape competitors’ websites to get an overview of their expansion plans in the interested areas and used this information to develop attractive retention offers to our customers. After the implementation of the bot, it increased retention by 40%.
Senior Full Stack Data Scientist
2019 - Present (3 years)
Worked with a large global research and advisory firms to design and develop data platform including the architecture, prototyping and development of data extract, transformation, cleansing and integration of structured and unstructured data
Developed optimal design of data warehouse environments, analyzing complex distributed data deployments and making recommendations to optimize performance.
Transformed raw data into useful information and insights utilizing Analytics, Business Intelligence (BI) and visualization tools.
Developed intricate algorithms based on deep dive statistical analysis and predictive data modeling which deepened relationships, strengthened longevity and personalized interaction with customers; leading to a 25% increase in customer satisfaction and 16% increase in sales.
Updated the company's data warehousing techniques - data recall and segmentation, resulting in a 30% increase in usability for non-technical staff members.
Developed an ETS for data sources used for reporting by sales, inventory, and marketing departments and modernized the data streamlining processes, reducing redundancy by 25%.
Built statistical models using historical data to conduct customer-based pricing and constructed several predictive models, such as bad debt and churn models, resulting in a 20% lower churn and 8% lower high-risk debtors.
Developed prediction algorithms using advanced data mining algorithms to classify similar properties together to develop sub-markets; each zip code is divided into sub-markets using advanced data mining techniques.
Refined personalization algorithms for 400K customers on web and mobile, boosting engagement and time spent on the platform by 25%.
Solved complex business problems using Machine Learning techniques like Regression, Classification, Supervised and Unsupervised Recommenders; increasing team efficiency by 20% and reducing costs by 27%.
Performed market analysis to efficiently achieve business objectives; increasing sales by 34%.
Utilized web scraping techniques to extract and organize competitor data for evaluation.
Researched and analyzed political systems in various countries, developed and implemented (social) media strategic plans and political proposals for clients.
Used R to create a matrix of political, demographic and household data to develop a set of predictive models that applied a score to every voter.
Identified voters who would be positively influenced by ads, mailings, social media and other outreach efforts, which resulted in local political parties winning several municipal and provincial seats.