Applied Data Scientist – Risk & Anomaly Detection

NEXBRIDGE RECRUITMENT PTE. LTD.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

NEXBRIDGE RECRUITMENT PTE. LTD. in Singapore seeks a Data Scientist/ML Engineer to analyse large datasets, develop anomaly detection, and build production-ready ML models that integrate with existing applications.

You will design robust data pipelines, evaluate model performance, and collaborate with engineers, product teams and domain experts to operationalise analytics and drive automation across engagements.

Qualifications

• Degree in Data Science, Computer Science or Statistics.
• Experience with large, messy operational datasets.
• Experience in applied data science including statistics, feature engineering, ML and model evaluation.
• Experience with anomaly detection, classification, clustering or risk modelling.
• Experience building data pipelines and productionising models.

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.

Skills

Python

SQL

Data pipelines

Feature engineering

Model evaluation

Analytical thinking

Domain collaboration

Communicate results

Big data

DevOps basics

Education

Bachelor's degree in Data Science, Computer Science, Statistics or related

Tools

Git

APIs

Docker

CI/CD

Job description

Job 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

Job 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 non-technical stakeholders
• Familiarity with containers, CI/CD or other DevOps / ModelOps practices would be advantageous
• Able to work independently in an ambiguous, fast-moving environment and collaborate closely with engineers, product teams and domain experts
• Strong sense of ownership for turning analysis into something that is used in an operational workflow

We regret that only shortlisted candidates will be notified.