FLINTEX CONSULTING PTE. LTD.
Data Scientist (Machine Learning)
- Compensation
- SGD 6K–SGD 8.5K / mo
- Location
- Singapore
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data science
- Posted
Skills
Must have
- • 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.
Nice to have
- • 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.
Full description
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.