Senior Data Scientist

DIGITAL BIZ SOLUTIONS PTE. LTD.; Singapore; On-site; SGD 150,000 - 210,000; Full time

Job summary

Digital Biz Solutions PTE. LTD. is seeking a Senior Data Scientist to design, build, and productionise AI/ML solutions across enterprise and government service operations.

This role emphasizes agentic AI, GenAI analytics, forecasting, anomaly detection, and strong MLOps ownership from problem framing to deployment. You should have extensive experience delivering production-grade ML, collaborating with cross-functional teams, and mentoring junior members while ensuring explainability and

Qualifications

• 10+ years in data science/machine learning roles with production delivery.
• Ownership of solutions from ideation to deployment and monitoring.
• Strong foundation in statistical modelling, hypothesis testing, and explainability.
• Hands-on experience with supervised/unsupervised learning (classification, regression, clustering, ensembles).
• GenAI/NLP experience with LLMs, RAG, and prompt engineering.
• MLOps, CI/CD, model governance, drift detection, retraining.
• Strong Python; API development for model inference (FastAPI/Flask).
• Cloud experience (Azure/AWS/GCP) and SQL with structured + unstructured data.
• Experience with vector databases and LangChain frameworks.

Responsibilities

• Lead end-to-end AI solution delivery from framing to deployment and monitoring.
• Build agentic AI and GenAI analytics solutions using RAG and LLM tooling.
• Develop forecasting and anomaly detection systems with actionable insights.
• Deliver production-grade ML with MLOps best practices and governance.
• Mentor junior team members and partner with cross-functional teams.

Skills

Statistical modelling

A/B testing

Explainability

Python

ML/AI delivery

GenAI/NLP

MLOps

Cloud platforms

SQL

API development

Tools

FastAPI/Flask

Git

CI/CD

LangChain/LangGraph/LlamaIndex

Vector databases

Pinecone

Spark/Databricks/Airflow

Job description

Senior Data Scientist (Agentic AI / GenAI Analytics) — Job Description

Role Overview

We are looking for a Senior Data Scientist to design, build, and productionise AI/ML solutions across enterprise and government service operations. This role focuses on agentic AI systems, GenAI-powered analytics, forecasting, anomaly detection, and strong end-to-end MLOps ownership—from problem framing to deployment and monitoring.

Key Responsibilities

• Lead end-to-end AI solution delivery

  • Own problem framing, data exploration, modelling, evaluation, deployment, and post-launch iteration.
  • Translate operational and business needs into measurable ML/AI outcomes with clear success metrics.

• Build agentic AI and GenAI analytics solutions

  • Design and implement agentic workflows for automated analysis and reporting.
  • Develop Retrieval-Augmented Generation (RAG) solutions using vector databases and modern LLM tooling.
  • Apply prompt engineering and (where needed) fine-tuning to improve task performance and reliability.

• Develop forecasting and anomaly detection systems

  • Build time-series forecasting models incorporating seasonality, trend, and calendar effects (e.g., public holidays).
  • Implement anomaly detection using statistical and ML approaches (e.g., prediction intervals, Isolation Forest).
  • Create actionable alerting logic aligned to operational thresholds and investigation capacity.

• Deliver production-grade ML with MLOps best practices

  • Implement CI/CD for ML, model versioning, governance, monitoring, drift detection, and retraining strategies.
  • Ensure explainability and stakeholder trust using SHAP/feature importance and clear model documentation.

• Stakeholder partnership and technical mentorship

  • Partner with operations, finance, and cross-functional teams to drive adoption and measurable impact.
  • Mentor junior team members on applied ML, experimentation, and production readiness.

Required Qualifications & Experience

• Experience

  • 10+ years in data science/machine learning / applied AI roles with proven production delivery.
  • Demonstrated ownership of solutions from ideation to production deployment and monitoring.

• Core ML & Statistics

  • Strong foundation in statistical modelling, hypothesis testing, A/B testing, calibration, and explainability.
  • Hands‑on experience with supervised/unsupervised learning (classification, regression, clustering, ensembles).

• GenAI / NLP

  • Practical experience building LLM-based solutions (agentic AI, RAG, prompt engineering).
  • Familiarity with modern GenAI frameworks and evaluation considerations (quality, safety, reliability).

• MLOps & Engineering

  • Experience with production ML platforms and practices (monitoring, drift detection, retraining, governance).
  • Strong Python skills; ability to build APIs/services for model inference (e.g., FastAPI/Flask).
  • Solid software engineering fundamentals (Git, CI/CD, modular design, testing).

• Cloud & Data

  • Experience with at least one major cloud ML ecosystem (Azure/AWS/GCP).
  • Strong SQL and experience working with structured + unstructured data stores.

Preferred Qualifications

• Experience delivering analytics/AI solutions in government, public sector, or regulated enterprise environments.
• Experience with vector databases and LLM orchestration frameworks (e.g., LangChain/LangGraph/LlamaIndex).
• Experience with fraud detection / imbalanced classification and precision/recall optimisation in real operations.
• Experience with Spark/Databricks/Airflow for scalable data pipelines and orchestration.
• Relevant certifications in ML/Cloud (e.g., AWS ML Specialty, Google Professional ML Engineer).

Tools & Tech Stack (Typical)

• Languages/Frameworks: Python, Scikit-learn, TensorFlow/PyTorch, Hugging Face
• GenAI: LLMs, RAG, LangChain/LangGraph, LlamaIndex, vector DBs (e.g., Pinecone)
• MLOps/Cloud: Azure ML / SageMaker / Vertex AI, CI/CD for ML, monitoring & drift detection
• Data: SQL/NoSQL, BigQuery/Redshift/S3/Cosmos DB, Databricks, Spark
• APIs & Workflow: FastAPI/Flask, Git, Airflow