Data Scientist

NSEARCH GLOBAL PTE. LTD.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Data Scientist role at NSEARCH GLOBAL PTE. LTD. is seeking professionals to analyse large operational datasets, develop anomaly detection and risk scoring approaches, and deploy production-ready ML models.

You will collaborate with domain experts to integrate analytical outputs into workflows, build scalable data pipelines, and monitor model performance and data quality across engagements. Experience with Python, SQL, and ML is essential.

Qualifications

• Degree in data science, CS, statistics, ML, analytics or IS preferred.
• Experience with large, messy operational datasets.
• Experience in applied data science including ML & model evaluation.
• Experience with anomaly detection, clustering or risk modelling.
• Experience building data pipelines and productionized models.
• Strong Python and SQL skills.
• Able to translate problems into analytical approaches; familiar with Git, testing & APIs.

Responsibilities

• Analyse large operational datasets to identify patterns, anomalies and risk indicators.
• Develop anomaly detection, clustering, segmentation and risk scoring approaches.
• Build supervised ML models where outcomes or labels exist.
• Perform feature engineering using structured and unstructured data.
• Design evaluation methods for model performance, including precision & recall.
• Work with domain experts to embed analytical outputs into workflows.
• Build and maintain data pipelines to support modelling at scale.
• Develop production-ready model or scoring pipelines and integrate with applications.
• Monitor model performance, data quality and data drift post-deployment.
• Conduct exploratory analysis to discover opportunities for workflow redesign and automation.
• Support the use of LLMs and other AI techniques alongside traditional ML where appropriate.

Skills

Python

SQL

Data analysis

Machine learning

Feature engineering

Education

Bachelor's degree in Data Science, Computer Science, Statistics, Machine Learning, Data Analytics or Information Systems

Tools

Git

APIs

Automated testing

Job description

Our client, one of Asia-Pacific’s leading organizations is looking for:

Data Scientist

Responsibilities

• Analyse large operational datasets to identify patterns, anomalies, outliers and risk indicators.
• Develop approaches for anomaly detection, clustering, segmentation and risk scoring.
• Build supervised machine-learning models where historical outcomes or labelled cases are available.
• Perform feature engineering using structured and unstructured data.
• Design evaluation approaches to assess model performance, including precision, recall, false positives and false negatives.
• Work with domain experts to determine how analytical outputs should be incorporated into operational workflows.
• Build and maintain data-processing and feature pipelines to support modelling at scale.
• Develop production-ready model or scoring pipelines and integrate them with applications built by the forward-deployed teams.
• Monitor model performance, data quality and data drift after deployment.
• Conduct exploratory analysis to identify new opportunities for workflow redesign and automation.
• Support the use of LLMs and other AI techniques alongside traditional statistical and machine-learning approaches where appropriate.
• Develop reusable analytical approaches and components that can be applied across multiple agency engagements.

Requirements

• Degree in Data Science, Computer Science, Statistics, Machine Learning, Data Analytics or Information Systems preferred.
• Experience working with large, messy operational or transactional datasets.
• Experience in applied data science, including statistics, feature engineering, machine learning and model evaluation.
• Experience with techniques such as anomaly detection, classification, clustering or risk modelling.
• Experience building data pipelines and taking models beyond experimentation into production environments.
• Strong proficiency in Python and SQL.
• Able to translate a business or operational problem into an analytical problem and determine the appropriate modelling approach.
• Familiarity with software engineering practices including Git, automated testing and APIs.
• Comfortable assessing model trade-offs and explaining them clearly to nontechnical stakeholders.