{"id":823819,"url":"https://alion.io/job/ncs-eg-data-scientist","title":"#EG Data Scientist","company":{"id":8907,"name":"NCS","domain":"ncs.co","url":"https://alion.io/company/ncs-co","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":82,"open_postings":376,"ghost_share":0.298,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":49,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","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":null,"salary_estimate":{"min_usd":51000,"max_usd":111000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":39},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"DeepSeek","optional":false},{"name":"Embeddings","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Qwen","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"Statsmodels","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"GCP","optional":true},{"name":"Matplotlib","optional":true},{"name":"MLFlow","optional":true},{"name":"NumPy","optional":true},{"name":"Power BI","optional":true},{"name":"Seaborn","optional":true},{"name":"Tableau","optional":true}],"status":"live","first_seen_at":"2026-08-21T04:41:59Z","employer_posted_date":"2026-08-21","last_verified_at":"2026-10-17T14:43:29Z","board_verified":true,"closed_at":null,"days_open":57,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":57},"description":"NCS is a leading AI Tech Services company. With a 15,000-strong team across the Asia Pacific, NCS scales its platforms and capabilities to provide clients with greater agility and AI expertise across a range of Industries. Embracing a strong ecosystem of global partners, NCS transforms technology services delivery combining AI with digital resilience to drive real business impact. NCS is a subsidiary of the Singtel Group.\n This role sits within NCS AI Central's (AIC) Forward Deployed Engineering (FDE) model - the combined capability that takes AI solutions from proof-of-concept through to hardened production systems. As Data Scientist, you apply statistical modelling, classical machine learning, and structured analysis to complement the squad's Gen AI work - validating problem framing with data, building baseline and comparison models, and ensuring Gen AI solutions are evaluated against rigorous, quantitative benchmarks rather than only qualitative judgement\nWhat will you do:\n1. Problem Framing & Statistical Analysis\nWork with SMEs and PMs to translate business problems into well-defined statistical/ML problems, including hypothesis definition and success metrics.\nPerform exploratory data analysis to understand distributions, correlations, and data quality issues before any model is proposed.\nAdvise when a classical ML or rules-based approach is more appropriate, defensible, or explainable than a Gen AI solution, and make that case clearly to stakeholders.\n2. Model Development & Validation\nBuild and validate classical ML models (regression, classification, clustering, time-series forecasting) as baselines or standalone solutions.\nApply rigorous statistical validation - train/test/holdout design, cross-validation, significance testing - to avoid overfitting and unsupported claims.\nWhere a Gen AI solution is in play, build the classical-ML or statistical baseline it must beat, so \"the LLM helped\" is a provable claim, not an assumption.\n3. Applied Gen AI Collaboration\nPartner with AI Engineers on evaluation design, contributing statistical rigor to benchmark and evaluation methodology.\nSupport feature engineering and structured-data pipelines that feed both classical models and Gen AI/RAG systems.\nMaintain working awareness of the broader Gen AI model landscape, including China-origin models (DeepSeek, Qwen, GLM), sufficient to design fair comparisons between classical and Gen AI approaches.\n4. FDE & Development/Maintenance Coverage\nDuring FDE engagements: rapidly build baseline models and statistical analyses to validate problem framing and set a quantitative bar for any Gen AI solution to clear.\nDuring system development & maintenance engagements: monitor model performance and data drift over time for any classical models in production, and support recalibration/retraining as needed.\n5. Collaboration\nWork closely with AI Engineers and the AI/LLM Specialist to ensure Gen AI outputs are compared fairly against rigorous statistical baselines.\nDocument methodology, assumptions, and results clearly for both technical and non-technical audiences.\nRole Levels We Are Hiring For\nWe are hiring at two levels for this role. All responsibilities above apply to both; the distinction is in scope of ownership, years of experience, and seniority of judgement expected.\nData Scientist\n4-5 years of hands-on experience in statistical modelling / classical machine learning. Builds and validates models for individual engagements, under guidance from a Senior Data Scientist or AI Architect.\nExecutes defined analysis and modelling tasks; escalates ambiguous problem-framing decisions to senior team members.\nSenior Data Scientist\n6+ years of hands-on experience, including prior ownership of statistical/ML strategy for complex or high-stakes problems. Owns problem framing and model validation approach across multiple engagements.\nAdvises stakeholders directly on when a classical or rules-based approach is more defensible than a Gen AI solution; mentors junior Data Scientists.\n We are hiring at two levels for this role. All responsibilities above apply to both; the distinction is in scope of ownership, years of experience, and seniority of judgement expected.\nData Scientist\n4-5 years of hands-on experience in statistical modelling / classical machine learning. Builds and validates models for individual engagements, under guidance from a Senior Data Scientist or AI Architect.\nExecutes defined analysis and modelling tasks; escalates ambiguous problem-framing decisions to senior team members.\nSenior Data Scientist\n6+ years of hands-on experience, including prior ownership of statistical/ML strategy for complex or high-stakes problems. Owns problem framing and model validation approach across multiple engagements.\nAdvises stakeholders directly on when a classical or rules-based approach is more defensible than a Gen AI solution; mentors junior Data Scientists.\nQualifications\nThe ideal candidate should possess:\n4+ years hands-on experience in statistical modelling / classical machine learning (see Role Levels for the split between Data Scientist and Senior Data Scientist).\nStrong grounding in statistics - hypothesis testing, regression, experimental design, causal inference basics.\nProficiency in Python (pandas, scikit-learn, statsmodels) and SQL.\nComfortable working with structured/tabular data at production scale, not just Gen AI-adjacent unstructured data.\nFamiliarity with Gen AI concepts (embeddings, RAG, prompting) sufficient to collaborate effectively with AI Engineers - not required to build LLM systems directly.\nWorking knowledge of the China AI model landscape (DeepSeek, Qwen, GLM) a plus, for informed cross-comparisons where relevant.\nPreferred Qualifications\nExperience with time-series forecasting or causal inference in a production setting.\nExposure to MLOps practices for classical model deployment/monitoring.\nPrior experience in a regulated or Government analytics context.\nFamiliarity with visualization/BI tooling (Tableau, Power BI, or similar) for stakeholder-facing reporting.\nTech Stack (Illustrative)\nLanguages: Python (pandas, scikit-learn, statsmodels, numpy), SQL\nModelling: Regression, classification, clustering, time-series (ARIMA/Prophet)\nMLOps (light): MLflow or equivalent experiment tracking\nVisualization: Matplotlib/Seaborn, Tableau/Power BI (where used)\nCloud: AWS/Azure/GCP; GCC/HCC exposure a plus\n Why Join NCS\nLead high-impact AI management consulting programs for major enterprises and public sector clients.\n\nShape enterprise strategies and governance frameworks that drive real transformation.\n\nWork with a talented, multidisciplinary team in a collaborative environment.\n\nCompetitive compensation and strong professional development support.\n\nWe are driven by our AEIOU beliefs-Adventure, Excellence, Integrity, Ownership, and Unity-and we seek individuals who embody these values in both their professional and personal lives. We are committed to our Impact: Valuing our clients, Growing our people, and Creating our future.\nTogether, we make the extraordinary happen.\nLearn more about us at ncs.co and visit our LinkedIn career site.\nScam Alert\nWe are aware of fraudulent job offers and impersonations of NCS recruiters. Phishing emails using convincing-looking but fake addresses are also commonly used to trick you into thinking that they come from official NCS sources.\nPlease note that all official communications from NCS Group will only be sent from verified corporate email addresses. Always check that the sender’s email address ends with the genuine NCS domain, @ncs.com.sg and beware of extra letters, symbols or misspellings. 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