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De 4 a 6 años
6-10 años
10-15 años
Más de 15 años
Data Engineer @Groundhog Technologies Inc.2021 ~ 2024
・
Data Analyst、Data Engineer、Data Scientist、Customer Experience Analyst
En un mes
to improve operational efficiency, and deploying monitoring solutions that have reduced Mean-Time-To-Repair (MTTR) by 40%. I have a strong foundation in SQL, especially PostgreSQL, which enables me to handle complex data analysis tasks effectively. My technical skill set is rounded out with proficiency in Python, Scala, Airflow for workflow management, Docker for containerization, and Linux shell scripting. I have led projects that improved client procurement efficiency by 15% and increased deployment rates by 60%, demonstrating my ability to leverage data insights to drive business improvements. With a Master's in Information Management and
Empleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
De 4 a 6 años
University of Illinois at Urbana-Champaign, School of Information Sciences
・
Information Management
Full stack software Develpoer @Avidbots India private Limited2023 ~ Presente
・
Software Engineer
En un mes
Experience Full stack software Developer • Avidbots India private Limited JanuaryPresent Acted as an integral member of scrum team and diligently adhered to agile methodologies throughout all phases of SDLC. Successfully navigated a complex API transition , meticulously migrating from monolithic architecture to a robust micro services-based framework, ensuring enhanced scalability, agility, and maintainability for the system. Revamped and restructured APIs in Node js , leveraging Stored procedures for enhanced efficiency and performance. Implemented APIs for customers and integrated subscription functionality based on JWT tokens to drive revenue generation. Actively participated in daily Scrum meetings to exchange status
Empleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
De 4 a 6 años
Vasavi college of Engineering
・
Bachelor of Engineering
Senior Front-End Engineer @TonFura2023 ~ Presente
・
Front-End engineer / Full-stack engineer
En un mes
Empleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
6-10 años
National Taiwan University
・
Computer Science and Information Engineering
資深主任工程師 @長青資訊2017 ~ Presente
・
資深工程師
En un mes
tasks, enhancing my ability to contribute meaningfully to my team and projects. I value a balanced approach to life, striving for concentration and efficiency in my work while cherishing relaxed and enjoyable moments outside of it. Technical Skills Back-End Development: Go : Demonstrated expertise in developing high-performance, scalable back-end services using Go. Proficient in leveraging Go’s concurrency model and its standard library to build efficient microservices architectures. Experienced in integrating Go applications with a variety of middleware and utilizing Go’s powerful interfaces for modular and maintainable code design. Frameworks & Libraries : Deep
Empleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
6-10 años
勤益科技大學
・
軟體工程
Past
Frontend Engineering Manager, Data Science @Vpon Big Data Group2022 ~ 2023
・
Frontend Engineer, Full Stack Engineer
En un mes
one to three members within a year. 2. Provided new engineer training with comprehensive development standards for quick integration. 3. Conducted code reviews to ensure quality and offer immediate feedback. 4. Contributed to system planning and front-end architecture decisions for security, stability, and scalability. 5. Implemented Git Flow and Github Actions for efficient team collaboration. 6. Wrote unit tests, E2E tests using Jest, Cypress, and Mocks Server for code and system stability. Sr. Frontend Engineer, Data Science • Vpon Big Data Group MayFebruary 2022 | Taipei, Taiwan 1.
Desempleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
10-15 años
YZU University (元智大學)
・
Information CommunicationManager @GOMAJI 夠麻吉2017 ~ Presente
・
Project Lead / Tech Lead / Team Lead / Technical Manager
En un mes
Empleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
10-15 años
Shih Hsin University
・
Management Information Systems, GeneralPast
Senior Back End Software Developer @PT Profeed Social Media Management2023 ~ 2024
・
Front-End / Back-End / Full Stack Web Developer / Software Engineer
En un mes
Desempleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
6-10 años
Universitas Pembangunan Nasional Veteran Jawa Timur
・
Informatics Engineering
Cloud Services Manager @AIA2021 ~ 2022
・
DevOps Engineer, Site Reliability Engineer
En un mes
Code Terraform Terragrunt Go Lang ARM Templates, AWS CloudFormation Database Administration PostgreSQL MongoDB DynamoDB MSSQL Scripting python Scripting Bash & Shall Scripting :: Work Experience:: Skyflow Inc. FebruaryPresent Engineering Lead | SRE A WS | Kubernetes | Argo CD | Istio | Go Lang | GitLabs | Terraform | Terragrant| PGsql Manage the overall design and implementation of secure, scalable, and fault-tolerant infrastructure Managing and Automating Infrastructure Using Terraform, Terragrunt and Go Scripting. Deploying and Managing K8s clusters on Multi-Tenant and Dedicated Environments Managing and Administrating PostgreSQL Databases on EKS Postgres S3 data backup & replication setup (CRR) KMS/Secrets replication setup (CMS) Monitoring - Infra, Application
Empleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
10-15 años
Sikkim Manipal University
・
Information Technology
Past
Senior iOS Developer @Rooit Inc. (XO App)2022 ~ 2023
・
iOS / Backend / DevOps
En un mes
to seamlessly bind view state and actions. - Implemented the NFT feature, including Rooit Alpha Cell and Roo. - Generated Xcode project using XcodeGen. - Maintained consistent coding style with SwiftFormat. - Introduced the Shop tab feature to enrich app functionality. - Modularized app features into smaller frameworks for improved scalability. - Wrote comprehensive UnitTest cases to ensure code robustness. For DevOps Responsibilities: - Constructed API Gateway services to integrate external client API requests. - Utilized Prometheus and Grafana for service monitoring. - Employed technologies such as K8S to reduce expenses on GCP. CTO • WoWFood (新場景股
Desempleado
・
Listo para la entrevista
A tiempo completo / Interesado en trabajar a distancia
De 4 a 6 años
武漢大學
・
中文Past
DeFi Engineer @OpenOcean Finance2023 ~ 2024
・
Blockchain Developer
En un mes
the network's high throughput and low fees to optimize user transactions. • Specialized in Rust programming for smart contract development, ensuring secure and efficient contract execution within the OpenOcean ecosystem. • Integrated Solana's unique features, such as Sealevel parallel processing, into OpenOcean's offerings, enhancing the platform's scalability and user experience. • Contributed to the design and implementation of cross-chain bridges, facilitating seamless asset transfers and expanding OpenOcean's interoperability within the DeFi space. • Engaged with the Solana developer community, staying updated with the latest ecosystem developments to incorporate cutting-edge solutions into OpenOcean's
Desempleado
・
Listo para la entrevista
A tiempo completo / Sólo a distancia
De 4 a 6 años
Kyiv National Taras Shevchenko University
・
Computer ScienceEl plan de reclutamiento más ligero y eficaz
Busque currículums y tome la iniciativa de ponerse en contacto con los solicitantes de empleo para lograr una mayor eficacia en la contratación. La elección de cientos de empresas.
- Examinar todos los resultados de la búsqueda
- Acceso ilimitado para iniciar nuevas conversaciones
- currículos accesibles sólo para empresas de pago
- Ver dirección de correo electrónico y números de teléfono de los usuarios
Consejos de búsqueda
1
Search a precise keyword combination
senior backend php
2
Use quotes to search for an exact phrase
"business development"
3
Use the minus sign to eliminate results containing certain words
UI designer -UX
Sólo los currículums públicos están disponibles con el plan gratuito.
Actualiza a un plan avanzado para ver todos los resultados de la búsqueda incluyendo decenas de miles de currículums exclusivos en CakeResume.
Definition of Reputation Credits
Technical Skills
Specialized knowledge and expertise within the profession (e.g. familiar with SEO and use of related tools).
Problem-Solving
Ability to identify, analyze, and prepare solutions to problems.
Adaptability
Ability to navigate unexpected situations; and keep up with shifting priorities, projects, clients, and technology.
Communication
Ability to convey information effectively and is willing to give and receive feedback.
Time Management
Ability to prioritize tasks based on importance; and have them completed within the assigned timeline.
Teamwork
Ability to work cooperatively, communicate effectively, and anticipate each other's demands, resulting in coordinated collective action.
Leadership
Ability to coach, guide, and inspire a team to achieve a shared goal or outcome effectively.
Professional Background
Situación actual
Empleado
Progreso en la búsqueda de empleo
Professions
Data Scientist, Machine Learning Engineer
Fields of Employment
Banca, Inteligencia Artificial / Aprendizaje Automático, AdTech / MarTech
Experiencia laboral
De 4 a 6 años
Management
Ninguno
Habilidades
Idiomas
English
・
Fluido
Job search preferences
Posición
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Tipo de trabajo
A tiempo completo
Ubicación
台灣台北, 台灣新北市
A distancia
Interesado en trabajar a distancia
Freelance
Sí, soy un autónomo amateur.
Educación
Escuela
政治大學
Mayor
統計
Education
National Chenchi University, MS, Statistics, 2015 – 2017
- GPA : 3.84 / 4.0
- Master Thesis: Entropy Based Feature Selection, Professor Pei-Ting, Chou
- Objective: Build a similarity matrix based on Mutual Entropy under Hierarchical Clustering. Afterwards, select clustered features as the final selection.
- Compare the model with other feature selection methods like RF, Lasso, F-score.
National Chen-Kung University, BS, Mathematics, 2011 – 2015
Skills
Programing
- Python
- Scala
- R
- MSSQL
Data-related Tools
- Tensorflow (Keras)
- PyTorch
- Spark
- Docker
- Scikit-Learn
- Pandas
Cloud Platform
- AWS
- GCP
Language
- English: TOEFL 98 / 120
Work Experience
CTBC Bank, Model Development Department, Data Scientist
2021.12 – present
- About the department:
- Responsible for developing models related to bank recommendations and risks, including projects such as coupon recommendations, account opening marketing lists, and fraud detection.
- Job responsibilities:
- Throughout the entire project lifecycle, my primary responsibilities included model design, model training, end-to-end process development, feature design, performance tracking, and method research.
Fraud Alert Project
- Objective:
- Predicting potential fraudulent accounts based on transaction data, restricting transactions in advance to prevent harm.
- Responsibilities/Achievements:
- Development and deployment of credit card and financial features.
- Managing the data flow process from receiving variables to model predictions, identifying risk factors, and updating alert lists.
- Implemented Autoencoder + contrastive learning to achieve a 1.81% improvement in model effectiveness.
Coupon Recommendation
- Objective:
- Personalized coupon recommendations for mobile banking users to increase click-through rates and redemption rates.
- Responsibilities/Achievements:
- Utilized multi-task learning to simultaneously predict click-through behavior and coupon redemptions, resulting in a 14% increase in click-through rate and a 74% increase in redemption rate.
- Created performance tracking reports to monitor online model performance and provide insights to Business Units.
Financial Product Recommendations
- Objective:
- Tailored financial product recommendations for mobile banking users to enhance click-through rates without compromising conversion rates.
- Responsibilities/Achievements:
- Applied multi-task learning to jointly learn click-through and conversion behaviors, fine-tuned model architecture, achieving a 90% outperformance against competitor models in online testing.
Marketing List for Digital Savings Accounts
- Objective:
- Optimized conversion rates for marketing lists related to digital savings accounts
- Responsibilities/Achievements:
- successfully raising conversion rates from 0.23% to 1.16%
Work Experience
CLICKFORCE, Data Engineer Supervisor, 2020.1 – 2021.11
- About the company:
- As a top domestic digital advertisement company, CLICKFORCE cooperates with over 900 web media and over 400 mobile media to build a huge advertising environment. CLICKFORCE considers data-driven solution as the core concept of the company, and dedicates to help advertisers to achieve their commercial goals.
- At 2020, CLICKFORCE won 2 awards at Agency & Advertiser of the Year.
- Successfully acquire the exclusive advertising agency qualification for Tokyo 2020 Olympics in Taiwan.
- Job responsibilities:
- Optimize ad performance from all aspects, including the system, target audience tags, etc.
- Do researches for new ML model (recommender model, NLP model) or architecture which is suitable for our system.
- Develop data-related products or projects.
- Analyze data to help improve our system or inspect whether the demands from business side is doable.
Real-time AD Recommender System
- Objective:
- Building a real-time ad recommender system to upgrade our ad server and get better performance.
- Responsibilities:
- Figure out what kind of recommender system components that is suitable for our ad system.
- Build a tower-like and feature-cross model refer to other famous recommender system model.
- Responsible for system engineering, which includes data preprocessing, embedding generates, memory cache, cold start, model API, etc.
Interest Tags
- Objective:
- Build interest tags for ads to help ad optimizers choose their target audience.
- Responsibilities:
- Create the features from what articles they saw, what website they viewed, and what ads they interacted.
- Deal with 20 million rows data and 120 million inference samples.
- Build ML model to predict each user's behavior on certain ads.
- Using Spark through AWS EMR to accelerate the speed of producing tags.
- Achievements:
- Raise CTR performance up to 200-300% of the original tags depends on different tags, and gain more impression while maintain better performance.
- After accomplishing this project, we terminated the cost on purchasing interest tags from other company, and successfully turned the original cost into revenue by providing profitable data.
First Party Cookie Mapping
- Objective:
- Deal with the Google 3rd party Cookie issue, figure out a method to map numerous 1st party Cookies to a user.
- Responsibility:
- Transform this problem into a ML mission. Design the label of the data, figure out what feature we can get or produce and whether the feature is useful for the goal.
- Apply XGboost on this mission.
- Build a small test to prove this method works.
- Achievement:
- 70% of precision.
- One of the solution of our company while the cancelation of 3rd party Cookie happen.
Invoice Data Application
- Objective:
- Develop invoice data application.
- Responsibility:
- Responsible for fine-tuning BERT to predict category for each product.
- Produce invoice data report to brands or business unit. It demonstrates the sales volume across different channel, what kind of products are frequently bought together, and also shows comparison of target brand to the other brands.
- Achievements:
- Produce an invoice data report product.
- Produce invoice tags for ad system.
Other Experience
E.Sun AI 2020 Summer Competition, 2020.7 – 2020.8
- Objective:
- Extract names of money laundering suspects from an article.
- Responsibilities:
- Crawl the articles from different media, and parse them by using Selenium, Requests, and Beautiful Soup.
- Construct 2-step model: First, identify whether the article is related to money laundering. Second, extract the suspects' names.
- Build model serving API by Tensorflow Serving.
- Build REST API for preprocessing request data and return the prediction.
- Achievement:
- 23rd place among 409 teams.
Youtube Data-Driven Marketing System, Institute for Information Industry, 2019.8 – 2019.11
- Objectives:
- Use the title and the description of videos to automatically classify videos.
- Use the title and the description of videos to identify whether a video is sponsored.
- Give suggestions for Youtubers or companies who desire to sponsor in a video based on data analysis.
- Responsibilities:
- Apply Google API and write Python functions to get structured raw data.
- Train word vectors using Gensim based on Wiki's open data.
- Use the frequency of each sentence as a criteria to eliminate useless words.
- Tune LSTM, Conv1D, BERT on the NLP mission.
- Use EDA methods to see the insights of the data under different classes and different sponsored status.
- Achievement:
- 71% accuracy in classifying video’s type.
- 89% accuracy in detecting sponsored content.
E.Sun Real Estate Price Prediction Competition, 2019.7 – 2019.8
- Objective:
- Use the real estate training data to build a model and predict the real estate price within 10% residual.
- Responsibilities:
- Apply XGBoost, LGBM and other ML models to train the model.
- Collect the outputs as new features from each ML model and add them into the original data set to enhance the performance of the final model.
- Achievement:
- 150th place out of 1200 teams.
KKTV Data Game，2017.5 – 2017.6
- Objective:
- Predict the next video a user watch in the next time interval.
- Responsibilities:
- Extract different features from raw data, such as the latest video, the video which got the longest viewing time, the video which got the largest number of viewing.
- Use the user viewing data to construct a similarity matrix of each video as additional features.
- Achievement:
- 10th place out of 50 teams.
MRT Open Data Competition, 2017.4 – 2017.5
- Objective:
- Study the changes of passenger volume of MRT by surrounding geometric data.
- Responsibilities:
- Apply bisection method to build the edges between MRT stations.
- Combine other geometric data based on these borders.
- Use Lasso feature selection method to explore the importance of each feature.
- Add noises into features to check the features are not randomly selected.
- Achievement:
- Certificate of Honorable Mention.
Education
National Chenchi University, MS, Statistics, 2015 – 2017
- GPA : 3.84 / 4.0
- Master Thesis: Entropy Based Feature Selection, Professor Pei-Ting, Chou
- Objective: Build a similarity matrix based on Mutual Entropy under Hierarchical Clustering. Afterwards, select clustered features as the final selection.
- Compare the model with other feature selection methods like RF, Lasso, F-score.
National Chen-Kung University, BS, Mathematics, 2011 – 2015
Skills
Programing
- Python
- Scala
- R
- MSSQL
Data-related Tools
- Tensorflow (Keras)
- PyTorch
- Spark
- Docker
- Scikit-Learn
- Pandas
Cloud Platform
- AWS
- GCP
Language
- English: TOEFL 98 / 120
Work Experience
CTBC Bank, Model Development Department, Data Scientist
2021.12 – present
- About the department:
- Responsible for developing models related to bank recommendations and risks, including projects such as coupon recommendations, account opening marketing lists, and fraud detection.
- Job responsibilities:
- Throughout the entire project lifecycle, my primary responsibilities included model design, model training, end-to-end process development, feature design, performance tracking, and method research.
Fraud Alert Project
- Objective:
- Predicting potential fraudulent accounts based on transaction data, restricting transactions in advance to prevent harm.
- Responsibilities/Achievements:
- Development and deployment of credit card and financial features.
- Managing the data flow process from receiving variables to model predictions, identifying risk factors, and updating alert lists.
- Implemented Autoencoder + contrastive learning to achieve a 1.81% improvement in model effectiveness.
Coupon Recommendation
- Objective:
- Personalized coupon recommendations for mobile banking users to increase click-through rates and redemption rates.
- Responsibilities/Achievements:
- Utilized multi-task learning to simultaneously predict click-through behavior and coupon redemptions, resulting in a 14% increase in click-through rate and a 74% increase in redemption rate.
- Created performance tracking reports to monitor online model performance and provide insights to Business Units.
Financial Product Recommendations
- Objective:
- Tailored financial product recommendations for mobile banking users to enhance click-through rates without compromising conversion rates.
- Responsibilities/Achievements:
- Applied multi-task learning to jointly learn click-through and conversion behaviors, fine-tuned model architecture, achieving a 90% outperformance against competitor models in online testing.
Marketing List for Digital Savings Accounts
- Objective:
- Optimized conversion rates for marketing lists related to digital savings accounts
- Responsibilities/Achievements:
- successfully raising conversion rates from 0.23% to 1.16%
Work Experience
CLICKFORCE, Data Engineer Supervisor, 2020.1 – 2021.11
- About the company:
- As a top domestic digital advertisement company, CLICKFORCE cooperates with over 900 web media and over 400 mobile media to build a huge advertising environment. CLICKFORCE considers data-driven solution as the core concept of the company, and dedicates to help advertisers to achieve their commercial goals.
- At 2020, CLICKFORCE won 2 awards at Agency & Advertiser of the Year.
- Successfully acquire the exclusive advertising agency qualification for Tokyo 2020 Olympics in Taiwan.
- Job responsibilities:
- Optimize ad performance from all aspects, including the system, target audience tags, etc.
- Do researches for new ML model (recommender model, NLP model) or architecture which is suitable for our system.
- Develop data-related products or projects.
- Analyze data to help improve our system or inspect whether the demands from business side is doable.
Real-time AD Recommender System
- Objective:
- Building a real-time ad recommender system to upgrade our ad server and get better performance.
- Responsibilities:
- Figure out what kind of recommender system components that is suitable for our ad system.
- Build a tower-like and feature-cross model refer to other famous recommender system model.
- Responsible for system engineering, which includes data preprocessing, embedding generates, memory cache, cold start, model API, etc.
Interest Tags
- Objective:
- Build interest tags for ads to help ad optimizers choose their target audience.
- Responsibilities:
- Create the features from what articles they saw, what website they viewed, and what ads they interacted.
- Deal with 20 million rows data and 120 million inference samples.
- Build ML model to predict each user's behavior on certain ads.
- Using Spark through AWS EMR to accelerate the speed of producing tags.
- Achievements:
- Raise CTR performance up to 200-300% of the original tags depends on different tags, and gain more impression while maintain better performance.
- After accomplishing this project, we terminated the cost on purchasing interest tags from other company, and successfully turned the original cost into revenue by providing profitable data.
First Party Cookie Mapping
- Objective:
- Deal with the Google 3rd party Cookie issue, figure out a method to map numerous 1st party Cookies to a user.
- Responsibility:
- Transform this problem into a ML mission. Design the label of the data, figure out what feature we can get or produce and whether the feature is useful for the goal.
- Apply XGboost on this mission.
- Build a small test to prove this method works.
- Achievement:
- 70% of precision.
- One of the solution of our company while the cancelation of 3rd party Cookie happen.
Invoice Data Application
- Objective:
- Develop invoice data application.
- Responsibility:
- Responsible for fine-tuning BERT to predict category for each product.
- Produce invoice data report to brands or business unit. It demonstrates the sales volume across different channel, what kind of products are frequently bought together, and also shows comparison of target brand to the other brands.
- Achievements:
- Produce an invoice data report product.
- Produce invoice tags for ad system.
Other Experience
E.Sun AI 2020 Summer Competition, 2020.7 – 2020.8
- Objective:
- Extract names of money laundering suspects from an article.
- Responsibilities:
- Crawl the articles from different media, and parse them by using Selenium, Requests, and Beautiful Soup.
- Construct 2-step model: First, identify whether the article is related to money laundering. Second, extract the suspects' names.
- Build model serving API by Tensorflow Serving.
- Build REST API for preprocessing request data and return the prediction.
- Achievement:
- 23rd place among 409 teams.
Youtube Data-Driven Marketing System, Institute for Information Industry, 2019.8 – 2019.11
- Objectives:
- Use the title and the description of videos to automatically classify videos.
- Use the title and the description of videos to identify whether a video is sponsored.
- Give suggestions for Youtubers or companies who desire to sponsor in a video based on data analysis.
- Responsibilities:
- Apply Google API and write Python functions to get structured raw data.
- Train word vectors using Gensim based on Wiki's open data.
- Use the frequency of each sentence as a criteria to eliminate useless words.
- Tune LSTM, Conv1D, BERT on the NLP mission.
- Use EDA methods to see the insights of the data under different classes and different sponsored status.
- Achievement:
- 71% accuracy in classifying video’s type.
- 89% accuracy in detecting sponsored content.
E.Sun Real Estate Price Prediction Competition, 2019.7 – 2019.8
- Objective:
- Use the real estate training data to build a model and predict the real estate price within 10% residual.
- Responsibilities:
- Apply XGBoost, LGBM and other ML models to train the model.
- Collect the outputs as new features from each ML model and add them into the original data set to enhance the performance of the final model.
- Achievement:
- 150th place out of 1200 teams.
KKTV Data Game，2017.5 – 2017.6
- Objective:
- Predict the next video a user watch in the next time interval.
- Responsibilities:
- Extract different features from raw data, such as the latest video, the video which got the longest viewing time, the video which got the largest number of viewing.
- Use the user viewing data to construct a similarity matrix of each video as additional features.
- Achievement:
- 10th place out of 50 teams.
MRT Open Data Competition, 2017.4 – 2017.5
- Objective:
- Study the changes of passenger volume of MRT by surrounding geometric data.
- Responsibilities:
- Apply bisection method to build the edges between MRT stations.
- Combine other geometric data based on these borders.
- Use Lasso feature selection method to explore the importance of each feature.
- Add noises into features to check the features are not randomly selected.
- Achievement:
- Certificate of Honorable Mention.