Junior Data Scientist (Python + Sql)

Optimum Solutions Pte Ltd; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Optimum Solutions Pte Ltd in Singapore is seeking a Data Scientist to support multiple product squads in redesigning operational workflows and developing AI-enabled solutions. The role focuses on analysing large operational datasets, identifying anomalies and risk patterns, and building machine-learning models to improve decision-making and operational efficiency.

The position entails building scalable data pipelines, deploying production-ready models, and collaborating with engineers, product

Qualifications

• Bachelor's degree or higher in a data-related field.
• Strong Python and SQL skills with experience on large datasets.
• Hands-on experience in applied data science including statistics, ML, and model evaluation.

Responsibilities

• Analyse large operational datasets to identify patterns, anomalies, outliers and potential risk indicators.
• Develop machine-learning models for anomaly detection, clustering, classification, segmentation and risk scoring.
• Perform feature engineering and evaluate model performance with appropriate metrics.
• Build and maintain scalable data pipelines and deploy production-ready models integrated with applications.
• Monitor model performance, data quality and data drift after deployment.
• Collaborate with engineers, product teams and domain experts to translate challenges into analytical solutions.
• Apply LLMs and other AI techniques alongside traditional ML approaches where appropriate.
• Develop reusable analytical solutions for cross-project use.

Skills

Python

SQL

Data analysis

Feature engineering

Machine learning

Model evaluation

Anomaly detection

Data pipelines

Production deployment

Git

APIs

Education

Degree in Data Science, Computer Science, Statistics or related field

Tools

Git

APIs

Job description

Our client is seeking a Data Scientist to support multiple product squads in redesigning operational workflows and developing AI-enabled solutions. The role focuses on analysing large operational datasets, identifying anomalies and risk patterns, and building machine-learning models to improve decision-making and operational efficiency.

Responsibilities

• Analyse large operational datasets to identify patterns, anomalies, outliers and potential risk indicators.
• Develop machine-learning models for anomaly detection, clustering, classification, segmentation and risk scoring.
• Perform feature engineering using structured and unstructured data, and evaluate model performance using appropriate metrics.
• Build and maintain scalable data pipelines and deploy production-ready models integrated with applications.
• Monitor model performance, data quality and data drift after deployment.
• Collaborate with engineers, product teams and domain experts to translate operational challenges into practical analytical solutions.
• Apply LLMs and other AI techniques alongside traditional machine-learning approaches where appropriate.
• Develop reusable analytical solutions and components for application across multiple projects.

Requirements

• At least 2 years of relevant experience in data science or related field
• Degree in Data Science, Computer Science, Statistics, Machine Learning, Data Analytics, Information Systems or a related field.
• Strong proficiency in Python and SQL, with experience analysing large, complex operational or transactional datasets.
• Hands-on experience in applied data science, including statistical analysis, feature engineering, machine learning and model evaluation.
• Experience with anomaly detection, classification, clustering, risk modelling or similar techniques.
• Experience building data pipelines and deploying models into production environments.
• Familiarity with software engineering practices, including Git, automated testing and APIs