Data Processing & Management:

• Develop and maintain scalable data processing pipelines for managing large datasets on both on-premise and cloud platforms (e.g., AWS).
• Ensure data integrity, consistency, and accuracy through thorough validation and cleansing methods.

Data Analysis & Insights:

• Continuously analyze data to uncover trends, patterns, and correlations within large datasets.

Generate actionable insights and communicate findings to stakeholders through clear and effective visualizations.

Model Development:

• Build, test, and deploy predictive models using (deep) machine learning algorithms with frameworks like TensorFlow, PyTorch, and Scikit-learn.
• Continuously monitor and optimize models to enhance performance and accuracy.

Collaboration and Support:

• Collaborate with data engineers, AI engineers, and software developers to understand data needs and provide technical assistance.
• Promote effective communication and teamwork within the AI and data teams, as well as with other technical departments.

Security and Compliance:

• Ensure secure and compliant data handling and processing by following best practices and meeting regulatory standards.
• Perform regular audits and evaluations to detect and address security risks.

Continuous Improvement:

• Identify opportunities to enhance data processing, analysis, and model development workflows.
• Keep up-to-date with the latest trends and advancements in data science and AI.

Job Requirements:

• Bachelor's degree in Data Science, Computer Science, Statistics, or a related field. A higher degree is preferred.
• At least 3-5 years of experience in data science, machine learning, or a related field.
• Demonstrated expertise in processing and analyzing large datasets.
• Solid experience in Python programming and using data manipulation libraries such as Pandas and NumPy.
• Expertise in machine learning frameworks and tools, including TensorFlow, PyTorch, and Scikit-learn.
• Strong analytical and problem-solving abilities with a keen, data-driven approach.
• Exceptional communication skills, capable of translating complex data insights into clear explanations for non-technical stakeholders.
• Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and big data technologies (e.g., Apache Spark, Hadoop).
• Strong attention to detail and a patient, systematic approach to data handling and analysis.

Preferred Qualifications:

• Experience in fraud detection, recommendation systems, and customer behavior prediction.
• Knowledge of data engineering and MLOps practices.
• Awareness of best practices for data security in various environments.
• Proficiency in data visualization tools such as Tableau and Power BI.

• A competitive salary package reflecting your skills, experience, and contributions.
• Support with accommodation, meals, and working visa arrangements.
• Holiday and New Year bonuses.
• Provision of essential work equipment (MacBook/iPhone)
• A professional, dynamic, and inclusive work environment.
• Financial support for home visits twice a year.