Data Scientist

The Data Scientist for the AI and Data Team will play a pivotal role in processing and analyzing large datasets, developing predictive models, and extracting actionable insights to drive business decisions. This position involves working closely with data engineers, AI engineers, and software developers to build and maintain robust data solutions that support our AI and data initiatives.

Key Responsibilities:

Data Processing & Management:

• Design, develop, and maintain scalable data processing pipelines for handling large datasets using both on-premise and cloud platforms (e.g., AWS).
• Ensure data quality, consistency, and accuracy through rigorous validation and cleansing processes.

Data Analysis & Insights:

• Monitor and perform comprehensive data analysis to identify trends, patterns, and correlations within large datasets.
• Generate actionable insights and present findings to stakeholders through clear and concise visualizations.

Model Development:

• Develop, test, and deploy predictive models with (deep) machine learning algorithms using frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Continuously monitor and refine models to improve performance and accuracy.

Collaboration and Support:

• Work closely with data engineers, AI engineers, and software developers to understand data requirements and provide technical support.
• Facilitate effective communication and collaboration within the AI and data teams and other technical teams.

Security and Compliance:

• Ensure the security and compliance of data handling and processing by implementing best practices and adhering to regulatory requirements.
• Conduct regular audits and assessments to identify and mitigate security vulnerabilities.

Continuous Improvement:

Identify areas for improvement in data processing, analysis, and model development workflows. Stay updated with the latest industry trends and technologies related to data science and AI.

Qualifications: Education:

Bachelor's degree in Data Science, Computer Science, Statistics, or a related field. A higher degree is a plus.

Experience:

• Minimum of 3-5 years of experience in data science, machine learning, or a similar role.
• Proven experience in processing and analyzing large datasets.
• Strong background in Python programming and data manipulation libraries (e.g., Pandas, NumPy).

Skills:

• Proficiency in machine learning frameworks and tools (e.g., TensorFlow, PyTorch, Scikit- learn).
• Strong analytical and problem-solving skills with a meticulous and data-sensitive approach.
• Excellent communication skills with the ability to convey complex data insights to non- technical stakeholders.
• Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and big data technologies (e.g., Apache Spark, Hadoop).
• Attention to detail and a patient, methodical approach to data handling and analysis.

Preferred Qualifications:

• Experience in fraud detection, recommendation systems, and predicting customer behavior.
• Understanding of data engineering and MLOps methodologies.
• Awareness of security best practices in data environments.
• Proficiency with data visualization software (e.g., Tableau, Power BI).

● Annual leave 15 days per year

● Health Insurances, social insurance

● Performance review: once per year

● 13th-month salary

● Internal training/sharing and professional Training courses

● Team building, company trip, year-end party, monthly activities,....

● Devices: Macbook and screen (If needed)

● Free tea and coffee

● Comfortable working Area

● Working hours: 9 am-6 pm from Monday to Friday