Data Scientist

RAPSYS TECHNOLOGIES PTE. LTD.; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

RAPSYS TECHNOLOGIES PTE. LTD. invites applications for a data science specialist focused on turning large operational datasets into actionable insights. You will prototype and productionize machine-learning models, perform feature engineering, and collaborate with engineers and domain experts.

The role emphasizes building scalable data pipelines, ensuring data quality, and integrating analytics into real-world workflows. Strong Python/SQL skills and experience with model evaluation are essential.

Qualifications

• Degree in data science, CS, statistics or ML; data analytics preferred.
• Experience with large, messy datasets and applied data science incl. feature engineering and model evaluation.

Responsibilities

• Analyze large operational datasets to identify patterns, anomalies, outliers, and risk indicators.
• Develop approaches for anomaly detection, clustering, segmentation, and risk scoring.
• Build supervised machine-learning models with historical labels where available.
• Perform feature engineering using structured and unstructured data.
• Design evaluation methods for model performance including precision and recall.
• Collaborate with domain experts to integrate analytical outputs into workflows.
• Build and maintain data-processing pipelines to support scalable modelling.
• Develop production-ready model and scoring pipelines integrated with apps.
• Monitor model performance, data quality and drift post deployment.
• Explore opportunities for workflow redesign and automation.
• Support use of LLMs and other AI techniques alongside traditional methods.
• Develop reusable analytical components for multiple agency engagements.

Skills

Python

SQL

Analytical problem solving

Model evaluation

Education

Degree in Data Science, Computer Science, Statistics, Machine Learning

Tools

Git

APIs

CI/CD

Job description

Key Responsibilities

• Analyze large operational datasets to identify patterns, anomalies, outliers, and risk indicators.
• Develop approaches for anomaly detection, clustering, segmentation, and risk scoring.
• Build supervised machine-learning models where historical outcomes or labeled cases are available.
• Perform feature engineering using structured and unstructured data.
• Design evaluation approaches to assess model performance, including precision, recall, false positives, and false negatives.
• Work with domain experts to determine how analytical outputs should be incorporated into operational workflows.
• Build and maintain data-processing and feature pipelines to support modelling at scale.
• Develop production-ready model or scoring pipelines and integrate them with applications built by the forward-deployed teams.
• Monitor model performance, data quality, and data drift after deployment.
• Conduct exploratory analysis to identify new opportunities for workflow redesign and automation.
• Support the use of LLMs and other AI techniques alongside traditional statistical and machine-learning approaches where appropriate.
• Develop reusable analytical approaches and components that can be applied across multiple agency engagements.

Requirements & Qualifications

A. Educational Qualification & Experience

• Education: Degree in Data Science, Computer Science, Statistics, Machine Learning,
• Data analytics or information systems preferred.

Experience:

• Experience working with large, messy operational or transactional datasets.
• Experience in applied data science, including statistics, feature engineering, machine learning and model evaluation.
• Experience with techniques such as anomaly detection, classification, clustering, or risk modeling.
• Experience building data pipelines and taking models beyond experimentation into production environments.

B. Technical & Professional Competencies

Essential (Must-Have)

• Strong proficiency in Python and SQL.
• Able to translate a business or operational problem into an analytical problem and determine the appropriate modelling approach.
• Familiarity with software engineering practices, including Git, automated testing, and APIs.
• Comfortable assessing model trade-offs and explaining them clearly to non-technical stakeholders.

Preferred (Advantageous)

• Familiarity with containers, CI/CD, or other DevOps/ModelOps practices would be advantageous.

C. Soft Skills & Communication Attributes

• Able to work independently in an ambiguous, fast-moving environment and collaborate closely with engineers, product teams, and domain experts.
• Strong sense of ownership for turning analysis into something that is used in an operational workflow.