{"id":3393194,"url":"https://alion.io/job/websparks-data-scientist-fraud-analytics-models-public-sector","title":"Data Scientist - Fraud Analytics Models (Public Sector)","company":{"id":252555,"name":"Websparks","domain":"websparks.sg","url":"https://alion.io/company/websparks","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"contractor","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":9000,"max":13000,"currency":"SGD","period":"month","gross":true,"usd_annual":121800},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Amazon Redshift","optional":true},{"name":"AWS","optional":true},{"name":"AWS Glue","optional":true},{"name":"CI/CD","optional":true},{"name":"pySpark","optional":true},{"name":"Spark","optional":true},{"name":"Tableau","optional":true}],"status":"live","first_seen_at":"2026-10-28T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-10-28T00:00:00Z","board_verified":false,"closed_at":null,"days_open":8,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":8},"description":"1-year contract, renewable\nGovernment project\nHybrid work arrangement\nRole Overview\nThe Data Scientist will join the Fraud Analytics Models (FAM) team within the Data Science and AI Division (DSAD). The role supports the maintenance and expansion of FAM 2.0, which detects misuse of public training grants.\nKey priorities include addressing the backlog of methodology and threshold reviews, building and operationalising the FAM 2.0 Technical Dashboard, and assessing how new schemes can be progressively included in FAM 2.0’s fraud-risk coverage.\nThe successful candidate will contribute to reliable fraud-risk analytics, clear methodologies and sustainable implementation, while helping build DSAD’s in-house capability.\nKey Responsibilities\nMethodology and threshold reviews: Review and recalibrate FAM 2.0 methodologies and thresholds for prioritised backlog items. This includes back-testing, impact assessment, analytical specifications, validation approaches, gap analyses and documentation.\n\nRisk model enhancement: Translate business, policy and scheme requirements into changes to fraud-risk scenarios, indicators, features, thresholds and analytical logic.\n\nTechnical Dashboard development: Build and enhance monitoring of model performance, including precision and recall, data drift and concept drift. Develop alert logic and visualisations, and support deployment. Design performance measures that account for delayed or incomplete investigation outcomes.\n\nNew scheme assessment: Assess new schemes’ structures, eligibility rules, claims processes and known fraud risks. Map relevant patterns to the existing FAM 2.0 risk taxonomy and recommend how each scheme can be brought into coverage.\n\nGovernance and stakeholder engagement: Maintain clear methodology and change records. Communicate findings, trade-offs, dependencies and recommended actions to programme owners, FAM stakeholders, engineers and management.\n\nDocumentation and knowledge transfer: Produce reproducible code, analyses and documentation throughout the engagement.\n\nRequirements and Qualifications\nEducation and ExperienceEducation: Degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics or a related quantitative discipline.\n\nExperience: 3–5 years of hands-on experience in data science, advanced analytics, model validation, fraud or risk analytics, or programme analytics.\n\nEssential Technical and Professional CompetenciesStrong hands-on proficiency in Python and SQL for data extraction, transformation and exploratory analysis, with experience building reproducible analytical workflows.\n\nHands-on experience developing, validating or enhancing predictive, risk-scoring, fraud-detection or anomaly-detection models. This includes feature development, back-testing, threshold calibration and performance evaluation using measures such as precision and recall.\n\nExperience translating business, policy or operational requirements into analytical specifications, model logic, indicators, rules or measurable risk hypotheses.\n\nExperience working with complex, large-scale datasets, including assessing data quality, tracing data lineage, investigating anomalies and documenting analytical assumptions.\n\nPreferred ExperienceExperience addressing technical challenges in fraud or risk modelling, including imbalanced data and delayed, incomplete or evolving outcome labels.\n\nExperience designing or implementing production model-monitoring approaches, including data drift, concept drift and alert thresholds.\n\nExperience building analytical dashboards using Tableau or an equivalent platform.\n\nExperience with CI/CD, MLOps controls or the operational deployment of analytical models.\n\nExperience with Spark or PySpark and enterprise data platforms, particularly AWS Redshift and AWS Glue.\n\nExperience with network or graph analytics to identify related entities or coordinated patterns.\n\nExperience working with workforce, training, grants, claims or regulatory datasets.\n\nPrior experience in Singapore public-sector engagements or familiarity with government data-classification requirements.\n\nSoft Skills and CommunicationStrong analytical and structured problem-solving skills, with a rigorous and detail-oriented approach.\n\nAbility to turn ambiguous requirements into clear assessment, methodology and implementation phases.\n\nClear written and verbal communication, including the ability to explain technical findings and trade-offs to non-technical stakeholders.\n\nAbility to integrate quickly into an existing team, working independently while collaborating across data science, engineering, platform, programme and business teams.\n\nProactive management of delivery risks, dependencies and competing business-as-usual and enhancement priorities.\n\n/Ref: 308\n\nSkills\nValidation, validation strategies, Dashboard Construction, Oracle Alerts, Operational Requirements, Technical Skills, Risk Modeling, Science and Math, Workflow Analysis, Computer Science, Fraud Analytics, Calibration Management, Backlog Management, Data Science, Education, Fraud Risk Management, Performance Monitoring","description_format":"text","description_chars":5108,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Government","Fraud Detection"],"lifecycle":[{"event":"open","at":"2026-10-28T10:28:00Z"}],"visa":[],"liveness":{"score":54,"band":"ok","label":"Likely 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