Data Scientist

Tap Growth ai; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Tap Growth ai is seeking a Data Scientist in Singapore to work on large, messy datasets and develop production-ready models. The role emphasizes turning analytical insights into operational solutions and collaborating with engineers and product teams.

The ideal candidate will have strong Python and SQL skills, experience in feature engineering, model evaluation, and communicating technical results to non-technical stakeholders. Experience with DevOps practices is a plus.

Qualifications

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

Responsibilities

• Build data pipelines and deploy models to production.
• Translate business problems into analytical modeling tasks.

Skills

Independent work

Ownership

Cross-functional collaboration

Education

Degree in Data Science or related field

Tools

Python

SQL

Git

APIs

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.