Data Scientist

HCL SINGAPORE PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

HCL SINGAPORE PTE. LTD. seeks a Data Scientist to support 4–5 product squads delivering AI-enabled solutions for government agencies.

You will work across the full data science lifecycle, from exploration to production deployment. You will build scalable data pipelines, develop predictive models, and collaborate with domain experts to embed analytics into workflows, with a focus on operational efficiency and risk detection.

Qualifications

• Bachelor's degree in Data Science, CS, Statistics or related field.
• 5+ years of experience with large-scale datasets.
• Hands-on experience in statistical analysis, feature engineering, ML, model evaluation.
• Experience with anomaly detection, classification, clustering, risk modeling.
• Experience deploying ML models into production environments.

Responsibilities

• Analyze large datasets to identify trends, anomalies, and risk indicators.
• Develop analytical solutions involving anomaly detection, clustering, segmentation, and risk-scoring methodologies.
• Build and evaluate supervised machine learning models using historical and labeled datasets.
• Perform feature engineering using structured and unstructured data sources.
• Design and execute model evaluation frameworks, including precision/recall analysis.
• Collaborate with domain experts to integrate outputs into workflows.
• Build and maintain scalable data processing pipelines.
• Develop and deploy production-ready models and scoring solutions.
• Monitor model performance, data quality, and data drift post-deployment.
• Conduct exploratory data analysis to uncover workflow optimization opportunities.
• Support adoption of LLMs and AI techniques alongside traditional ML.

Skills

Python

SQL

Data Science

Machine Learning

Communication

Education

Bachelor’s degree in Data Science or related field

Tools

Git

APIs

CI/CD

Docker

Job description

About the role

We are seeking a Data Scientist to support 4–5 product squads delivering AI-enabled solutions and workflow transformation initiatives for government agencies. This role focuses on leveraging large-scale operational data to uncover insights, identify anomalies and risk patterns, and develop predictive models that enhance decision-making and operational efficiency. The ideal candidate will be comfortable working across the full data science lifecycle, from exploratory analysis and model development to building scalable data pipelines and deploying production-ready solutions.

Key responsibilities

• Analyze large operational datasets to identify trends, anomalies, outliers, and risk indicators.

• Develop analytical solutions involving anomaly detection, clustering, segmentation, and risk-scoring methodologies.

• Build and evaluate supervised machine learning models using historical and labeled datasets.

• Perform feature engineering using both structured and unstructured data sources.

• Design and execute model evaluation frameworks, including precision, recall, false-positive, and false-negative analysis.

• Collaborate with domain experts to integrate analytical outputs into operational workflows and business processes.

• Build and maintain scalable data processing and feature engineering pipelines.

• Develop and deploy production-ready models and scoring solutions integrated with applications used by delivery teams.

• Monitor model performance, data quality, and potential data drift post-deployment.

• Conduct exploratory data analysis to uncover opportunities for workflow optimization and automation.

• Support the adoption of Large Language Models (LLMs) and other AI techniques alongside traditional statistical and machine learning approaches.

• Develop reusable analytical frameworks, assets, and components that can be leveraged across multiple government agency engagements.

Requirements & Qualifications

• Bachelor's degree in Data Science, Computer Science, Statistics, Machine Learning, Data Analytics, Information Systems, or a related discipline.

• At least 5 years of experience working with large-scale, complex operational or transactional datasets.

• Hands‑on experience in applied data science, including statistical analysis, feature engineering, machine learning, and model evaluation.

• Familiarity with techniques such as anomaly detection, classification, clustering, and risk modeling.

• Experience building robust data pipelines and deploying machine learning models into production environments.

Mandatory Skills

• Strong proficiency in Python and SQL.

• Ability to translate business or operational challenges into analytical problems and identify the most suitable modeling approach.

• Familiarity with software engineering best practices, including Git, APIs, and automated testing.

• Strong understanding of model performance trade-offs and the ability to communicate complex analytical concepts to non‑technical stakeholders.

Preferred Skills

• Exposure to containerization technologies, CI/CD pipelines, and DevOps/ModelOps practices.

• Experience with modern AI technologies, including Generative AI and Large Language Models (LLMs).

Soft Skills

• Strong problem‑solving and critical‑thinking abilities.

• Ability to work independently in a dynamic and ambiguous environment.

• Effective collaboration skills with engineers, product teams, and business stakeholders.

• Strong ownership mindset with a focus on delivering practical, scalable solutions that drive operational outcomes.

• Excellent communication and stakeholder management skills.