Data Scientist

Rapsys Technologies Pte Ltd.; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

Rapsys Technologies Pte Ltd. is seeking a Data Scientist to turn data into actionable insights. You will work with large, messy datasets, design and evaluate models, and collaborate with engineers and product teams to deploy data-driven solutions.

The role requires strong Python and SQL, experience in ML, data pipelines, and the ability to translate business problems into analytical approaches. Preference for familiarity with containers and CI/CD; you will own analytics end-to-end from

Qualifications

• Degree in Data Science, Computer Science, Statistics, Machine Learning, Data Analytics, or Information Systems preferred.
• Experience with large, messy operational or transactional datasets.
• Applied data science including statistics, feature engineering, model evaluation.
• Experience with anomaly detection, classification, clustering, or risk modelling.
• Experience building data pipelines and productionizing models.

Responsibilities

• Build and evaluate models on large datasets.
• Develop data pipelines and productionize models.
• Collaborate with engineers, product teams and domain experts.
• Translate business problems into analytical approaches and explain trade-offs to stakeholders.

Skills

Python

SQL

Git

APIs

Modeling & ML

Education

Degree in Data Science/CS/Statistics

Tools

None

Job description

Data Scientist

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 modelling.
  • Experience building data pipelines and taking models beyond experimentation into production environments.

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.

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.