Applied Data Scientist – Risk & Anomaly Detection

Job ResponsibilitiesAnalyse large operational datasets to identify patterns, anomalies, outliers and risk indicatorsDevelop approaches for anomaly detection, clustering, segmentation and risk scoringBuild supervised machine-learning models where historical outcomes or labelled cases are availablePerform feature engineering using structured and unstructured dataDesign evaluation approaches to assess model performance, including precision, recall, false positives and false negativesWork with domain experts to determine how analytical outputs should be incorporated into operational workflowsBuild and maintain data-processing and feature pipelines to support modelling at scaleDevelop production-ready model or scoring pipelines and integrate them with applications built by the forward-deployed teamsMonitor model performance, data quality and data drift after deploymentConduct exploratory analysis to identify new opportunities for workflow redesign and automationSupport the use of LLMs and other AI techniques alongside traditional statistical and machine-learning approaches where appropriateDevelop reusable analytical approaches and components that can be applied across multiple agency engagementsJob RequirementsDegree in Data Science, Computer Science, Statistics, Machine Learning, Data Analytics or Information Systems preferredExperience working with large, messy operational or transactional datasetsExperience in applied data science, including statistics, feature engineering, machine learning and model evaluationExperience with techniques such as anomaly detection, classification, clustering or risk modellingExperience building data pipelines and taking models beyond experimentation into production environmentsStrong proficiency in Python and SQLAble to translate a business or operational problem into an analytical problem and determine the appropriate modelling approachFamiliarity with software engineering practices including Git, automated testing and APIsComfortable assessing model trade-offs and explaining them clearly to non-technical stakeholdersFamiliarity with containers, CI/CD or other DevOps / ModelOps practices would be advantageousAble to work independently in an ambiguous, fast-moving environment and collaborate closely with engineers, product teams and domain expertsStrong sense of ownership for turning analysis into something that is used in an operational workflowInterested candidates are invited to apply via the “Apply Now” button.We regret that only shortlisted candidates will be notified.By submitting your personal data and CV, you consent to Nexbridge Recruitment Pte Ltd collecting, using and disclosing your personal data for recruitment purposes.Nexbridge Recruitment Pte LtdEA License No: 26C3616EA Personnel: Ching Ning XinRegistration No: R22108651