Requirement:
To strengthen data-driven decision-making, model development, and AI implementation across business units.
The position will be responsible for developing, training, and deploying machine learning models, ensuring production-grade
reliability and performance.
- Increasing demand for AI automation, recommendation systems, and predictive analytics within the company's digital transformation roadmap.
4.
Excellent knowledge and hands-on experience with Python, SQL, and data libraries such as Pandas, NumPy, Scikit-learn, and
TensorFlow/PyTorch
- Strong understanding of
machine learning algorithms, model evaluation, and feature engineering
6.
Experience with data visualization tools
(e.g., Power BI, Tableau, Plotly, or Matplotlib/Seaborn)
7.
Working knowledge of cloud-based AI platforms
(AWS SageMaker, GCP Vertex AI, or Azure ML)
8.
Familiarity with data pipelines, ETL, and MLOps concepts
(CI/CD for ML, Docker, Airflow, MLflow, etc.)
- Knowledge of big data frameworks (Spark, Databricks, or similar) is a
plus
10.
Solid understanding of data governance, security, and privacy compliance
11.
Training or certification
in Machine Learning, Deep Learning, or Data Engineering preferred
12.
Familiarity with Agile development and MLOps practices
Job Description:
Collect, clean, and preprocess large structured and unstructured datasets
Develop, validate, and deploy machine learning and AI models for prediction, classification, clustering, and recommendation
Collaborate with engineering and business teams to translate requirements into data-driven insights and AI solutions
Monitor and optimize deployed models to maintain performance and accuracy
Build and maintain data pipelines and feature stores for production models
Conduct exploratory data analysis (EDA) to identify trends and business opportunities
Document methodologies and present findings clearly to non-technical stakeholders
Support the development of AI strategy and contribute to innovation initiatives
Competencies and Personal Character:
A. Strong analytical and problem-solving mindset
B. Detail-oriented, curious, and data-driven
C. Communicative and collaborative across cross-functional teams
D. Able to manage multiple projects and meet deadlines under pressure
E. Self-motivated and proactive in exploring new AI technologies
F. Uphold data ethics, confidentiality, and accuracy
Additional Notes:
Minimum 3 years of experience as Data Scientist or Machine Learning Engineer
Proven record of deploying AI/ML models into production environments