#EG Data Scientist

Company Description

NCS is a leading AI Tech Services company. With a 15000-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.

Job Description

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 hand-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 hand-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

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 hand-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 hand-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 hand-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.

Prior experience in a regulated or Government analytics context.

Familiarity with visualization/BI tooling (Tableau, Power BI, or similar) for stakeholder-facing reporting.

Tech Stack (Illustrative)

Languages: Python (pandas, scikit-learn, statsmodels, numpy), SQL

Modelling: Regression, classification, clustering, time-series (ARIMA/Prophet)

MLOps (light): MLflow or equivalent experiment tracking

Visualization: Matplotlib/Seaborn, Tableau/Power BI (where used)

Cloud: AWS/Azure/GCP; GCC/HCC exposure a plus

Additional Information

Why Join NCS

Lead high-impact AI management consulting programmes for major enterprises and public sector clients.

Shape enterprise strategies and governance frameworks that drive real transformation.

Work with a talented, multidisciplinary team in a collaborative environment.

Competitive compensation and strong professional development support.

We 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.

Together, we make the extraordinary happen.

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