13 Database Architect jobs in Vietnam

Position: CAD Data & 3D Modeling - IT

Job Code:

- Working location: Binh Thanh District, HCMC

- Monthly Salary: Negotiable

Responsibility

CAD Data Analysis:

• Research and development for automating 2D drawing generation from CAD data.
• Understanding CAD data structures to research and develop methods for extracting essential elements such as holes, overall dimensions, and bends.

Multimodal Embedding for CAD and 2D Drawings:

• Research and development on associating and performing similarity searches between data of different modalities (3D and 2D).

Automated CAD Data Generation:

• Research and development aiming for automatic CAD data generation from prompts by creating CAD commands using generative models.

Product Integration with CAD Software:

• Development for integrating our products with major CAD software like SolidWorks and NX, enabling users to utilize our functionalities directly within the CAD environment.

Requirements

・Language: English - Business Level

*ability to complete daily tasks in English, including text communication and meetings. (CEFR B1 or Higher level)

・Experience:

• Experience in 3D data analysis.
• Experience with team development using Git and CI/CD (e.g., GitHub Actions).
• Basic understanding and hands-on experience with container technologies like Docker.
• Must currently reside in Vietnam or have plans to relocate. Foreign nationals must also hold a valid Vietnam work permit or be legally eligible to work in Vietnam.

・Knowledge:

• Foundational knowledge of algorithms related to machine learning, statistics, linear algebra, and computer science.

Experience:

• Experience in CAD plugin development or development using CAD SDKs.
• Experience working with 3D or 2D CAD/drawing data.
• Experience with releasing and operating machine learning models in a production environment.
• Experience with infrastructure building and operation using cloud platforms like Google Cloud or AWS.
• Experience with designing, developing, and operating large-scale data processing platforms.

Knowledge

• Knowledge of graphics libraries such as WebGL, OpenGL, Metal, or Vulkan.
• Familiarity with GPU-accelerated parallel computing programming (e.g., CUDA, OpenCL).

NOTICE: Only shortlisted candidates will be approached by RGF's consultant. Your resumes will be recorded in our system, and you will receive our Job Introduction Auto-email with suitable jobs in the coming time. Please check your email to get our vacant job. Thanks so much

Job Type: Full-time

Application Deadline: 2025/09/20

• Assist in the development, testing, and deployment of financial models and machine learning algorithms.
• Perform data extraction, cleaning, and transformation from various financial datasets.
• Conduct exploratory data analysis to identify trends and patterns.
• Collaborate with senior data scientists and stakeholders to define project requirements.
• Implement and tune machine learning models for predictive analytics.
• Visualize data and communicate findings effectively through reports and presentations.
• Ensure the quality and integrity of data used in modeling.
• Stay updated on the latest advancements in data science and financial modeling techniques.
• Support the maintenance and enhancement of existing analytical models.

• Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative field.
• Strong understanding of statistical concepts and machine learning algorithms.
• Proficiency in programming languages like Python or R and relevant libraries (e.g., Pandas, NumPy, scikit-learn).
• Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Tableau).
• Basic knowledge of financial markets and quantitative finance is a plus.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and teamwork abilities.

• Develop and implement advanced statistical and machine learning models for financial forecasting and risk assessment.
• Analyze large, complex financial datasets to identify patterns, trends, and insights.
• Build and deploy predictive models for areas such as credit scoring, fraud detection, and market analysis.
• Collaborate with finance and business teams to define project requirements and translate them into data science solutions.
• Perform data exploration, feature engineering, and model validation.
• Communicate complex analytical findings and model results to stakeholders.
• Stay abreast of the latest advancements in data science, machine learning, and financial analytics.
• Mentor junior data scientists and contribute to best practices.
• Optimize model performance and ensure scalability.
• Work independently and manage project timelines effectively in a remote environment.

• Master's or Ph.D. in Statistics, Mathematics, Computer Science, Economics, or a related quantitative field.
• 5+ years of experience in data science, with a focus on financial modeling and quantitative analysis.
• Strong programming skills in Python or R, and proficiency with SQL.
• Extensive experience with machine learning algorithms and libraries (e.g., Scikit-learn, TensorFlow, Keras).
• Experience working with large datasets and big data technologies (e.g., Spark).
• Familiarity with cloud platforms (AWS, Azure, GCP).
• Excellent analytical, problem-solving, and communication skills.
• Proven ability to work independently and deliver results in a remote setting.

• Design, develop, and implement complex actuarial models using statistical software and programming languages.
• Analyze large datasets to identify trends, patterns, and correlations relevant to insurance risk.
• Build predictive models for pricing, reserving, underwriting, and claims management.
• Apply machine learning algorithms and data mining techniques to enhance model accuracy and performance.
• Validate and test models to ensure their reliability and accuracy.
• Collaborate with actuaries, underwriters, and product managers to translate business needs into analytical solutions.
• Communicate complex analytical findings clearly and concisely to technical and non-technical stakeholders.
• Stay up-to-date with the latest advancements in data science, machine learning, and actuarial science.
• Contribute to the development of data infrastructure and analytical tools.
• Ensure compliance with regulatory requirements and industry best practices.

• Master's or PhD in Statistics, Mathematics, Actuarial Science, Data Science, or a related quantitative field.
• Minimum of 5 years of experience in data science or actuarial roles within the insurance industry.
• Proficiency in statistical modeling, machine learning techniques, and programming languages such as Python, R, or SQL.
• Experience with actuarial software and tools is highly desirable.
• Strong understanding of insurance principles, risk management, and financial modeling.
• Excellent analytical, problem-solving, and critical thinking skills.
• Proven ability to work with large and complex datasets.
• Strong communication and interpersonal skills, with the ability to present findings effectively.
• Experience with data visualization tools (e.g., Tableau, Power BI) is a plus.
• Ability to work effectively in a hybrid work environment, balancing remote and in-office collaboration.

• Assist in the collection, cleaning, and preprocessing of large datasets from various sources.
• Collaborate with the team to understand business requirements and translate them into data science problems.
• Develop, train, and evaluate predictive models using machine learning algorithms.
• Perform exploratory data analysis (EDA) to identify trends, patterns, and insights.
• Implement data visualization techniques to communicate findings effectively.
• Write clean, efficient, and well-documented code in Python or R.
• Participate in model deployment and monitoring activities under supervision.
• Stay updated with the latest developments in data science and machine learning.
• Contribute to team discussions and knowledge sharing sessions.
• Assist in the preparation of reports and presentations summarizing project outcomes.
• Learn and adhere to best practices in data management and privacy.
• Support senior team members on ad-hoc data analysis tasks.

• Currently pursuing or recently completed a Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.
• Solid understanding of statistical concepts and machine learning algorithms (e.g., regression, classification, clustering).
• Proficiency in at least one programming language commonly used in data science, such as Python or R.
• Familiarity with data science libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch).
• Basic knowledge of SQL for data retrieval.
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.
• Ability to work independently and manage time effectively in a remote setting.
• Eagerness to learn and a passion for data.
• Previous project experience or coursework in data science is a plus.

• Designing, building, and maintaining predictive models for various insurance products and business lines.
• Performing in-depth data analysis to identify patterns, trends, and anomalies related to risk factors.
• Validating model performance using rigorous statistical methods and back-testing.
• Implementing models into production environments and monitoring their ongoing performance.
• Collaborating with actuarial teams, underwriters, and business stakeholders to understand risk appetite and translate business needs into data science solutions.
• Developing and presenting clear, concise reports and visualizations of model findings to both technical and non-technical audiences.
• Keeping abreast of the latest advancements in data science, machine learning, and risk management techniques.
• Mentoring junior data scientists and contributing to the team's knowledge sharing.
• Ensuring compliance with industry regulations and internal data governance policies.
• Contributing to the development of data infrastructure and tooling for enhanced model development and deployment.

• A Master's or Ph.D. in a quantitative field such as Statistics, Mathematics, Computer Science, Economics, or a related discipline.
• A minimum of 6 years of experience in data science, with a strong focus on risk modeling within the financial services or insurance industry.
• Expertise in statistical modeling, machine learning algorithms (e.g., regression, classification, time series, survival analysis, deep learning), and experimental design.
• Proficiency in programming languages such as Python or R, and experience with data manipulation and analysis libraries (e.g., Pandas, NumPy, SciPy).
• Experience with SQL and working with large relational databases.
• Familiarity with cloud computing platforms (AWS, Azure, GCP) and big data technologies (e.g., Spark).
• Excellent understanding of insurance principles, risk management frameworks, and regulatory requirements (e.g., Solvency II, IFRS 17).
• Strong analytical and problem-solving skills, with meticulous attention to detail.
• Effective communication and presentation skills, capable of explaining complex technical concepts to diverse audiences.
• Proven ability to work independently and manage projects effectively in a remote setting.

• Assist in collecting, cleaning, and pre-processing large datasets from various sources.
• Collaborate with the team to perform exploratory data analysis and identify trends and patterns.
• Support the development and implementation of machine learning models for prediction and classification tasks.
• Conduct statistical analysis to validate model performance and interpret results.
• Help in creating visualizations and reports to communicate findings to stakeholders.
• Learn and apply various data science tools and programming languages (e.g., Python, R).
• Participate in team meetings, discussions, and knowledge-sharing sessions.
• Contribute to the documentation of data science processes and methodologies.
• Stay updated with the latest advancements in data science and machine learning.
• Assist in A/B testing and experiment design.

• Develop and implement advanced statistical and machine learning models for financial forecasting, risk assessment, and portfolio optimization.
• Clean, process, and analyze large, complex datasets from various financial sources.
• Identify key drivers and patterns within financial data to inform business strategy.
• Collaborate with cross-functional teams, including portfolio management, risk, and product development, to understand their data needs and deliver actionable insights.
• Design and conduct experiments to test hypotheses and evaluate model performance.
• Communicate complex findings and model results clearly and concisely to both technical and non-technical stakeholders through reports and presentations.
• Stay abreast of the latest advancements in data science, machine learning, and financial modeling techniques.
• Develop and maintain data pipelines and productionize machine learning models.
• Mentor junior data scientists and contribute to the team's technical growth.
• Ensure data integrity, accuracy, and adherence to data governance policies.

• Master's or Ph.D. in Data Science, Statistics, Computer Science, Economics, Mathematics, or a related quantitative field.
• Minimum of 5 years of experience as a Data Scientist, with a strong focus on financial applications.
• Expertise in programming languages such as Python or R, and proficiency with relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch, pandas, NumPy).
• Strong SQL skills and experience working with relational databases.
• Proven experience in building and deploying machine learning models in a production environment.
• Deep understanding of statistical modeling, experimental design, and data mining techniques.
• Knowledge of financial markets, instruments, and common financial analysis techniques is essential.
• Excellent problem-solving abilities and analytical thinking.
• Strong communication and presentation skills, with the ability to convey technical information effectively.
• Experience with big data technologies (e.g., Spark, Hadoop) is a plus.
• Demonstrated ability to work independently and manage projects in a remote setting.

• Leading the design, development, and implementation of advanced statistical and machine learning models for financial applications.
• Guiding a team of data scientists, providing technical leadership, mentorship, and project oversight.
• Collaborating with business stakeholders to identify key challenges and opportunities where data science can provide solutions.
• Performing in-depth data analysis, feature engineering, and model selection for diverse datasets.
• Ensuring the rigor, accuracy, and scalability of developed models through comprehensive validation and testing.
• Developing and maintaining documentation for models, methodologies, and results.
• Staying abreast of the latest research and advancements in data science, machine learning, and financial analytics.
• Communicating complex technical findings and recommendations effectively to both technical and non-technical audiences.
• Contributing to the strategic direction of the data science function within the organization.
• Ensuring compliance with regulatory requirements and ethical considerations in data usage and model development.