Data Scientist

RAPSYS TECHNOLOGIES PTE LTD; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

RAPSYS TECHNOLOGIES PTE LTD is seeking a Data Scientist to analyse large and complex datasets, build ML models, and translate business problems into actionable analytical solutions. The role involves applying statistics, building data pipelines, and deploying models into production to support operational decision-making and business outcomes.

Responsibilities include data cleaning, feature engineering, model evaluation, and collaboration with engineers and product teams to deliver practical data

Qualifications

• Degree in data science, computer science, statistics or related field.
• Experience with large, complex operational or transactional datasets.
• Applied data science including statistics, feature engineering, ML and evaluation.
• Experience with anomaly detection, classification, clustering, risk modelling.
• Strong Python and SQL skills.
• Experience building data pipelines and deploying ML models to production.
• Familiarity with Git, automated testing, and APIs.
• Ability to translate requirements into analytical solutions and explain findings to non-technical stakeholders.
• Ability to work independently in a fast-paced environment and collaborate cross-functionally.

Responsibilities

• Analyse large, complex, unstructured datasets to identify patterns, trends, and insights.
• Translate challenges into analytical problems and select appropriate ML techniques.
• Perform data cleaning, preprocessing, feature engineering, and EDA.
• Develop, evaluate, and optimise ML models (anomaly detection, classification, clustering, risk modelling).
• Assess model performance and communicate findings to non-technical stakeholders.
• Build and maintain data pipelines to support analytics and ML workflows.
• Deploy models into production and support their ongoing use in operations.
• Apply software engineering practices, including Git, automated testing, and API integration.
• Collaborate with engineers, product teams, and domain experts to deliver data science solutions.
• Take ownership of analytical solutions and ensure insights/models are integrated into operations.

Skills

Python

SQL

Data Science

Statistics

Machine Learning

Feature Engineering

Anomaly Detection

Classification

Clustering

Risk Modelling

Model Evaluation

Data Analysis

Data Pipelines

Model Deployment

APIs

CI/CD

DevOps

ModelOps

Education

Bachelor's degree in Data Science, Computer Science, Statistics, or related field

Tools

Git

Job description

We're Hiring: Data Scientist!

We are looking for a Data Scientist to analyse large and complex operational and transactional datasets, develop machine learning models, and translate business problems into actionable analytical solutions. The role involves applying statistical techniques, building data pipelines, and deploying models into production to support operational decision-making and business outcomes.

Job Description

We are looking for a Data Scientist to analyse large and complex operational and transactional datasets, develop machine learning models, and translate business problems into actionable analytical solutions. The role involves applying statistical techniques, building data pipelines, and deploying models into production to support operational decision-making and business outcomes.

Key Responsibilities

• Analyse large, complex, and unstructured operational or transactional datasets to identify patterns, trends, and insights.




• Translate business and operational challenges into analytical problems and select appropriate statistical and machine learning techniques.




• Perform data cleaning, preprocessing, feature engineering, and exploratory data analysis.




• Develop, evaluate, and optimise machine learning models using techniques such as anomaly detection, classification, clustering, and risk modelling.




• Assess model performance, evaluate trade-offs, and communicate findings and recommendations to non-technical stakeholders.




• Build and maintain data pipelines to support analytics and machine learning workflows.




• Deploy models beyond experimentation into production environments and support their ongoing use in operational processes.




• Apply software engineering practices, including Git, automated testing, and API integration.




• Collaborate with engineers, product teams, and domain experts to deliver practical data science solutions.




• Take ownership of analytical solutions and ensure that insights and models are effectively integrated into operational workflows.

Job Requirements

• Degree in Data Science, Computer Science, Statistics, Machine Learning, Data Analytics, Information Systems, or a related field.




• Experience working with large, complex operational or transactional datasets.




• Practical experience in applied data science, including statistics, feature engineering, machine learning, and model evaluation.




• Experience with anomaly detection, classification, clustering, risk modelling, or related analytical techniques.




• Strong proficiency in Python and SQL.




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




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




• Ability to translate business requirements into analytical solutions and explain technical findings clearly to non-technical stakeholders.




• Ability to work independently in an ambiguous, fast-paced environment while collaborating with cross-functional teams.

Preferred Skills

• Experience with containers and containerisation technologies.




• Familiarity with CI/CD pipelines and DevOps practices.




• Exposure to ModelOps or machine learning model deployment and management practices.

Key Skills

Python, SQL, Data Science, Statistics, Machine Learning, Feature Engineering, Anomaly Detection, Classification, Clustering, Risk Modelling, Model Evaluation, Data Analysis, Data Pipelines, Model Deployment, Git, Automated Testing, APIs, CI/CD, DevOps, ModelOps.