Senior Data Scientist

digital biz solutions pte. ltd.; Singapore; On-site; SGD 180,000 - 280,000; Full time

Job summary

Digital Biz Solutions Pte. Ltd. in Singapore seeks a Senior Data Scientist to design, build, and productionise AI/ML solutions across enterprise and government operations, with a focus on agentic AI and GenAI analytics. You will own end-to-end delivery from framing to deployment and monitoring.

You will lead GenAI workflows, implement MLOps, ensure explainability, and mentor junior engineers while collaborating with cross-functional teams to drive measurable impact.

Qualifications

• 10+ years in data science / ML / applied AI with production delivery.
• Demonstrated ownership from ideation to deployment and monitoring.
• Strong foundation in statistical modelling and hypothesis testing.
• Experience building LLM-based solutions and GenAI frameworks.

Responsibilities

• Lead end-to-end AI solution delivery across programs.
• Build agentic AI and GenAI analytics solutions with RAG workflows.
• Develop forecasting and anomaly detection systems.
• Deliver production-grade ML with MLOps including monitoring and governance.
• Mentor junior team members and partner with cross-functional teams.

Skills

Data science leadership

Python

MLOps

GenAI/NLP

Stakeholder management

Tools

LangChain

Pinecone

FastAPI

Git

CI/CD

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