Data Scientist (Machine Learning)
Posted
Summary
Flintex is hiring a Data Scientist (Machine Learning) in Singapore to build, train, and deploy predictive models and data pipelines that improve business operations and digital transformation. Core stack: Python (Pandas, NumPy, Scikit-learn), TensorFlow/PyTorch/XGBoost, SQL, cloud platforms (AWS/Azure/GCP), and BI tools like Power BI/Tableau.
About the Role
We are seeking a Data Scientist (Machine Learning) to develop and deploy advanced analytics and machine learning solutions that support business operations and digital transformation initiatives. The successful candidate will work closely with cross-functional teams to analyze large datasets, build predictive models, and deliver actionable insights to improve operational efficiency and business performance.
Key Responsibilities
• Develop, train, validate, and deploy machine learning models for predictive analytics and optimization.
• Analyze structured and unstructured datasets to identify trends, patterns, and business opportunities.
• Design and implement data pipelines for data collection, cleansing, feature engineering, and model training.
• Build forecasting, classification, regression, clustering, and anomaly detection models.
• Collaborate with business stakeholders to understand requirements and translate them into data-driven solutions.
• Evaluate model performance and continuously improve model accuracy and reliability.
• Develop dashboards and reports to communicate insights and recommendations.
• Work with data engineers to integrate machine learning models into production systems.
• Ensure data quality, governance, and compliance with organizational standards.
• Research and evaluate new machine learning algorithms and emerging technologies.
Requirements
• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline.
• 3–8 years of experience in Data Science, Machine Learning, or Advanced Analytics.
• Strong programming skills in Python (Pandas, NumPy, Scikit-learn).
• Experience with machine learning frameworks such as TensorFlow, PyTorch, or XGBoost.
• Strong knowledge of supervised and unsupervised learning techniques.
• Experience with SQL and relational databases.
• Familiarity with cloud platforms such as AWS, Azure, or GCP.
• Experience with data visualization tools such as Power BI or Tableau.
• Knowledge of Git, Docker, and MLOps concepts is an advantage.
• Strong analytical, problem-solving, and communication skills.
Preferred Skills
• Experience in time-series forecasting and predictive maintenance.
• Knowledge of optimization techniques and operations research.
• Experience with big data technologies such as Spark or Hadoop.
• Familiarity with Generative AI and Large Language Models (LLMs) is a plus.
• Experience working in the utilities, energy, manufacturing, or industrial sectors is highly desirable.
Key Competencies
• Strong analytical and statistical thinking.
• Ability to communicate complex technical concepts to non-technical stakeholders.
• Excellent problem-solving and critical thinking skills.
• Self-motivated with the ability to work independently and in a collaborative team environment.
• Strong stakeholder management and project delivery skills.
Skills
- AI
- Analytics
- Anomaly Detection
- AWS
- Azure
- Cloud
- Data Pipelines
- Data Quality
- Data Science
- Data Visualization
- Docker
- Feature Engineering
- GCP
- Generative AI
- Git
- Hadoop
- LLM
- Machine Learning
- MLOps
- NumPy
- pandas
- Power BI
- Predictive Analytics
- Python
- PyTorch
- scikit-learn
- Spark
- SQL
- Stakeholder Management
- Statistics
- Tableau
- TensorFlow
- Time Series
- XGBoost