I am Wen-Kai Chung
A professional data scientist specialized in time-series and NLP.
I develop models with robust object-oriented-programming (OOP) principles, monitor them with MLOps mindset, and deploy them in cloud environment. Employers and clients are happy about my working results because they can achieve not only better model performance but also the efficient model versioning and the balance between performance and stability. Checkout my projects and their feedbacks below!
Data science is the field I thoroughly enjoy, where we extract valuable insights and enhance our clients’ lives through data-driven automation and decision-making. Download my CV below to see my ability in time-series, NLP, and causal inference!
Residence
Germany
Code Exp.
5+ years
wkaichungtw@mail.com
Work Exp.
3+ years
Residence
(+49) 15222157727
Leadership
2+ years
Experience & Academics
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May 2022 – Mar 2023
Data Science Intern & Thesis Student
Lufthansa Technik- Implemented Hierarchical Flight Hours Forecast model, beating company’s existing model and getting lots of business attention.
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Jun 2021 – Apr 2022
Data Science Intern
Forecasty.AI- Led the beta launch of the AutoML platform.
- Deliver a model that successfully converted a big company to long-term client, laid the foundation for future projects, and paved the way for additional clients.
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Feb 2018 – Dec 2018
Data Science Researcher
Institute of Information Science, Academia Sinica- Published an NLP paper in HCII 2019 related to supply chain finance.
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Sep 2019 – Feb 2023
MS Economics
Universität Mannheim- DAAD Graduation Scholarship
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Sep 2010 – Jun 2014
Double Degree Mathematics & Economics
National Taiwan University- Presidential Awards x2
- Chien Shih-Liang Scholarship
Highlights
Time Series
I specialize in time series forecasting, with hands-on experience in hierarchical modeling, mixed-frequency models, and forecast interval generation techniques such as bootstrapping and conformal prediction. I’ve delivered impactful projects in aviation and industrial supply chains, currently supporting hierarchical demand forecasting at my company. Previously, I helped Forecasty.AI secure a major long-term client and drove visibility for Lufthansa Technik through data-driven decision support.
Natural Language Processing
I published a paper in 2019 to classify the commercial relationship of any given pair of companies in supply chains. This success is based on my solid knowledge of name entity recognition, knowledge graph, and domain knowledge. I now practice combining time series and NLP together to deliver better services.
MLOps
Not just the model performance, but also the model stability and monitoring plays a crucial role in earning clients’ trust. Having worked in a DevOps team for machine learning service, I experienced developing end-to-end ML pipelines with continuous integration and delivery, including model monitoring and retraining for best practice.
Industrial Organization
I finished MS Economics competition and regulation track, which builds firm theory of various forms of price and product competitions based on game theory. This secret weapon helps me not only on my daily work for data engineering, but also on business occasions when firm needs to take decision in response to prevailing market conditions.
Causal Inference
As businesses grow more cautious in decision-critical projects, there’s a rising demand for interpretable models—especially in areas like price and demand forecasting. With graduate training in causal inference, I apply methods such as impulse response analysis to help organizations understand model behaviour and make well-informed decisions.
Advanced Skills
Reading paper, implementing innovative ideas, and collaborating with the DevOps team are part of my daily job. Leveraging my BS Math background, I develop efficient and clean code in short time, and have impressed my employers with my quick-learning ability and open-minded attitude.
Works
Master Thesis
Evaluation of Hierarchical Model with Top-down Alignment against Transformer for Time Series Forecasting
I designed and experimented 10+ models for hierarchical flight hours forecasting. It outperforms company’s existing model, gains lots of business attention, and lays the foundation for similar projects.
Commodity Price Forecasting
NLP Paper in Supply Chain Finance
Upstream, Downstream or Competitor? Detecting Company Relations for Commercial Activities.
The paper aims at classifying the commercial relationship between any given pair of companies in industrial supply chains: upstream, downstream, parallel collaboration, and no-relation.
We proposed a novel word embedding scheme by building a multi-relational graph, outperforming Wor2Vec and GloVe.
Testimonials
Wen-Kai has quickly demonstrated deep knowledge in time series modeling and software engineering and therefore has stood out taking on additional responsibilities as a technical lead of our AutoML modeling pipeline and the beta release of our online platform.
We appreciate Mr. Chung for his dedicated attitude and ample contribution to our products. We love to work with him and strongly recommend him as part of your vision in the future.
Dr. Rahul K M
CEO, Forecasty.AI
Wen-Kai is a talented young data scientist with exceptional statistical and AI knowledge, capable of developing high-performing models to support business processes. His creativity, tenacity, and hard-working are valuable to solve complex problems.
Beyond their technical prowess, Wen-Kai is an invaluable team player. He is also mature enough even to work independently, and report results to the leadership.
Erica Fonseca
Delivery Solutions Architect,
Databricks
It has been a pleasure to work together with Wen-Kai. Apart from his impressive technical ability, he showed lots of enthusiasm and interest for any topic assigned to him and contributed greatly to the team with his willingness to help others and a collaborative mindset.
David Guo
Data Scientist,
Lufthansa Technik
Wen-Kai is a highly competent and diligent programmer and a good friend. The crawler he coded for me fulfilled all its goals and well exceeded my expectations. Thanks to him my research project could finally move forward. I would definitely love to work with him again in the future!
Minrui Gong
Doctoral Student,
Universität Mannheim
Certificates
IBM: DevOps and Software Engineering
The track contains 14 courses and a total of 160 learning hours for DevOps and Software Engineering skills. E.g., version control, CI/CD, containerization, and cloud deployment. The tools and concepts covered are Git, GitHub, Docker, Kubernetes, Jenkins, Agile, and cloud platforms like AWS and Azure.,
Deeplearning.AI: GenAI with LLM
With a total of 16 hours and 3 assignments, the course provides deep understanding of different strategies of improving LLM output, e.g., 0,1,N-shot prompt engineering, full vs PEFT prompt tuning, and Reinforcement Learning with Human Feedback via PPO policy.
Deeplearning.AI: NLP Track
The track contains 4 courses, 16 projects, and a total of 120 learning hours. It gives hands-on experience with key NLP techniques including text classification, sequence labeling, machine translation, and attention mechanisms. The track covered essential tools and models such as Word2Vec, RNNs, GRUs, Transformers, and beam search using TensorFlow.
Deeplearning.AI: Deep Learning Track
The track contains 5 courses, 20 projects, in total of 120 learning hours. It introduces foundations in neural networks, deep learning architectures, and practical model optimization. The program covers CNNs, RNNs, LSTMs, hyperparameter tuning, batch normalization, and deep learning deployment, using TensorFlow.
DataCamp: NLP Track
The track contains 6 courses, and a total of 25 learning hours.
DataCamp: Three Career Tracks
It contains data scientist, ML scientist, and data engineer career tracks, comprising 65 courses, 6 projects, and a total of 254 learning hours.
Udemy: Data Science with DAGs
The course introduces causal data science using directed acyclic graphs (DAGs), which combine graph theory and statistics for clear causal reasoning. I have learned how to isolate true causal effects, like education’s impact on salary, while handling biases such as confounding or selection bias (e.g., gender, city, home wealthiness etc). Keywords: Do-Calculus, Front-/Back-door Criteria, Z-identification, Transportability theory.
