Hi, I am applying for a master in Artificial Intelligence program, would love your brutal and honest feedback for this motivation letter!
My name is Reza, and I am applying for the Master's in Artificial Intelligence at Vrije Universiteit Amsterdam (VU). My motivation for pursuing this degree is shaped by my professional journey, strengthened by my academic foundation, and guided by a long-term vision to contribute to AI-driven solutions in healthcare.
Over the past five years, I have built a strong analytical career across the tech and financial industries. In my first three years at X, I worked as a Data Analyst and Business Intelligence specialist, where I built a rule-based data quality model that standardized text cleanup and parsed sales amounts. This model helped improve the performance of barcode-matching and quantity-estimation models built by the data science team, enhancing the overall commercialization quality of retail data for consumer-goods clients. I also built a sales performance dashboard that was used by retailers across regions in south-east Asia, improving their sales and inventory strategies. In the following two years at X, a consumer finance company, I focused on product funnel analysis and developed an RFM-based customer segmentation model that became the foundation for a clustering approach capturing key user behaviors for strategic marketing communication. I then evaluated these initiatives through A/B testing, helping identify communication strategies that support responsible borrowing and advance financial inclusion for underbanked users in Indonesia.
Throughout these experiences, I realized that my most intellectually fulfilling work came from involvement in data-science oriented projects. The research-driven nature and the scalability of the solutions that created greater impact on the business strengthened my reason to pursue a data science role. While my analytical background provides a solid foundation, I recognize that developing advanced and novel predictive modelling requires deeper theoretical and practical training in artificial intelligence. This gap motivates me to pursue a Master's in Artificial Intelligence at VU.
The previous academic journey in the bachelor program also became the root of my interest in AI. During my informatics study, I first gained experience in different expertises, where I developed my passion in coding by becoming a practicum assistant for programming subjects, and human-computer interaction where I was interning at X, developed and revamped a chatbot feature to improve usability. However, joining the Data Mining Laboratory is what introduced me to data science. This also led me to choosing data science specialization, where I completed a thesis titled Detecting Depressive Behavior with Sentiment Analysis, applying Natural Language Processing (NLP) to address mental-health challenges. Completing this thesis, from ethically scraping self-reported depressive posts, cleaning unstructured text, building features using Information Gain and TF-IDF, to training a Multinomial Naive Bayes model that achieves 94% recall, strengthened my interest in AI, particularly its applications in healthcare issues. At the same time, I completed a data science internship at Telkom, where I worked on a text-analysis project supporting decision-making for a smart-city government initiative, further reinforcing my motivation to pursue this field.
The VU's Master in Artificial Intelligence, provides the right direction that aligns with my career vision. I will choose the AI for Health track as it includes two key courses that will equip me with the right skills in AI. The Machine Learning Reasoning for Health will equip me with the ability to build models that generate medically sound, interpretable reasoning to support clinicians in making informed decisions. Furthermore, VU's emphasis in hybrid intelligence will enable trustworthy AI which resonate strongly with my vision of using it to complement human expertise. Equally, Multi-Agent Systems will expand my understanding of strategic interaction through reinforcement learning and game-theory, where it will enable me in designing intelligent systems that operate in complex environments. Furthermore, I envision myself conducting research under the supervision of Professor Annette ten Teije. Her work on Machine Learning Explainability in Breast Cancer Survival, addressed the low explainability in AI models, aligns with the goal that I hope to make. Learning from her will substantially help me to develop accountable algorithms that clearly articulate their decision boundaries and minimize the risk of overlooking clinically valuable information.
My long-term career goal is to address global challenges through AI, particularly in healthcare. I chose this field because of my previous experience in bachelor thesis, and the alarming global challenges, where the healthcare workforce is projected to face a shortage of at least 10 million workers globally by 2030, according to McKinsey. This issue, combined with the rapid advancement of generative AI, creates an urgent need for intelligent systems that can ease clinicians' workload while ensuring reliable and responsible AI-driven decision-making. Over the next decade, I aim to contribute to this solution by gaining best practices in the Netherlands, one of the world's leaders in healthcare innovation.
My name is Reza, and I am applying for the Master's in Artificial Intelligence at Vrije Universiteit Amsterdam (VU). My motivation for pursuing this degree is shaped by my professional journey, strengthened by my academic foundation, and guided by a long-term vision to contribute to AI-driven solutions in healthcare.
Over the past five years, I have built a strong analytical career across the tech and financial industries. In my first three years at X, I worked as a Data Analyst and Business Intelligence specialist, where I built a rule-based data quality model that standardized text cleanup and parsed sales amounts. This model helped improve the performance of barcode-matching and quantity-estimation models built by the data science team, enhancing the overall commercialization quality of retail data for consumer-goods clients. I also built a sales performance dashboard that was used by retailers across regions in south-east Asia, improving their sales and inventory strategies. In the following two years at X, a consumer finance company, I focused on product funnel analysis and developed an RFM-based customer segmentation model that became the foundation for a clustering approach capturing key user behaviors for strategic marketing communication. I then evaluated these initiatives through A/B testing, helping identify communication strategies that support responsible borrowing and advance financial inclusion for underbanked users in Indonesia.
Throughout these experiences, I realized that my most intellectually fulfilling work came from involvement in data-science oriented projects. The research-driven nature and the scalability of the solutions that created greater impact on the business strengthened my reason to pursue a data science role. While my analytical background provides a solid foundation, I recognize that developing advanced and novel predictive modelling requires deeper theoretical and practical training in artificial intelligence. This gap motivates me to pursue a Master's in Artificial Intelligence at VU.
The previous academic journey in the bachelor program also became the root of my interest in AI. During my informatics study, I first gained experience in different expertises, where I developed my passion in coding by becoming a practicum assistant for programming subjects, and human-computer interaction where I was interning at X, developed and revamped a chatbot feature to improve usability. However, joining the Data Mining Laboratory is what introduced me to data science. This also led me to choosing data science specialization, where I completed a thesis titled Detecting Depressive Behavior with Sentiment Analysis, applying Natural Language Processing (NLP) to address mental-health challenges. Completing this thesis, from ethically scraping self-reported depressive posts, cleaning unstructured text, building features using Information Gain and TF-IDF, to training a Multinomial Naive Bayes model that achieves 94% recall, strengthened my interest in AI, particularly its applications in healthcare issues. At the same time, I completed a data science internship at Telkom, where I worked on a text-analysis project supporting decision-making for a smart-city government initiative, further reinforcing my motivation to pursue this field.
The VU's Master in Artificial Intelligence, provides the right direction that aligns with my career vision. I will choose the AI for Health track as it includes two key courses that will equip me with the right skills in AI. The Machine Learning Reasoning for Health will equip me with the ability to build models that generate medically sound, interpretable reasoning to support clinicians in making informed decisions. Furthermore, VU's emphasis in hybrid intelligence will enable trustworthy AI which resonate strongly with my vision of using it to complement human expertise. Equally, Multi-Agent Systems will expand my understanding of strategic interaction through reinforcement learning and game-theory, where it will enable me in designing intelligent systems that operate in complex environments. Furthermore, I envision myself conducting research under the supervision of Professor Annette ten Teije. Her work on Machine Learning Explainability in Breast Cancer Survival, addressed the low explainability in AI models, aligns with the goal that I hope to make. Learning from her will substantially help me to develop accountable algorithms that clearly articulate their decision boundaries and minimize the risk of overlooking clinically valuable information.
My long-term career goal is to address global challenges through AI, particularly in healthcare. I chose this field because of my previous experience in bachelor thesis, and the alarming global challenges, where the healthcare workforce is projected to face a shortage of at least 10 million workers globally by 2030, according to McKinsey. This issue, combined with the rapid advancement of generative AI, creates an urgent need for intelligent systems that can ease clinicians' workload while ensuring reliable and responsible AI-driven decision-making. Over the next decade, I aim to contribute to this solution by gaining best practices in the Netherlands, one of the world's leaders in healthcare innovation.
