#EG Data Scientist

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. 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 What will you do: 1. Problem Framing & Statistical Analysis • Work with SMEs and PMs to translate business problems into well-defined statistical/ML problems, including hypothesis definition and success metrics. • Perform exploratory data analysis to understand distributions, correlations, and data quality issues before any model is proposed. • Advise 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. 2. Model Development & Validation • Build and validate classical ML models (regression, classification, clustering, time-series forecasting) as baselines or standalone solutions. • Apply rigorous statistical validation — train/test/holdout design, cross-validation, significance testing — to avoid overfitting and unsupported claims. • Where 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. 3. Applied Gen AI Collaboration • Partner with AI Engineers on evaluation design, contributing statistical rigor to benchmark and evaluation methodology. • Support feature engineering and structured-data pipelines that feed both classical models and Gen AI/RAG systems. • Maintain 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. 4. FDE & Development/Maintenance Coverage • During 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. • During system development & maintenance engagements: monitor model performance and data drift over time for any classical models in production, and support recalibration/retraining as needed. 5. Collaboration • Work closely with AI Engineers and the AI/LLM Specialist to ensure Gen AI outputs are compared fairly against rigorous statistical baselines. • Document methodology, assumptions, and results clearly for both technical and non-technical audiences. Role Levels We Are Hiring For 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. Data Scientist • 4–5 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. • Executes defined analysis and modelling tasks; escalates ambiguous problem-framing decisions to senior team members. Senior Data Scientist • 6+ 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. • Advises stakeholders directly on when a classical or rules-based approach is more defensible than a Gen AI solution; mentors junior Data Scientists. 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. Data Scientist • 4–5 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. • Executes defined analysis and modelling tasks; escalates ambiguous problem-framing decisions to senior team members. Senior Data Scientist • 6+ 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. • Advises stakeholders directly on when a classical or rules-based approach is more defensible than a Gen AI solution; mentors junior Data Scientists. Qualifications The ideal candidate should possess: • 4+ years hands-on experience in statistical modelling / classical machine learning (see Role Levels for the split between Data Scientist and Senior Data Scientist). • Strong grounding in statistics — hypothesis testing, regression, experimental design, causal inference basics. • Proficiency in Python (pandas, scikit-learn, statsmodels) and SQL. • Comfortable working with structured/tabular data at production scale, not just Gen AI-adjacent unstructured data. • Familiarity with Gen AI concepts (embeddings, RAG, prompting) sufficient to collaborate effectively with AI Engineers — not required to build LLM systems directly. • Working knowledge of the China AI model landscape (DeepSeek, Qwen, GLM) a plus, for informed cross-comparisons where relevant. Preferred Qualifications • Experience with time-series forecasting or causal inference in a production setting. • Exposure to MLOps practices for classical model deployment/monitoring. • P…