Data Science Intern

Calibrax; Kuala Lumpur; On-site; MYR 11,160 - 22,320; Full time

Job summary

Calibrax is seeking a Data Science Intern to support engineering and delivery teams with preparing, exploring, and modelling data for internal systems and tooling. You will work with real datasets, real constraints, and real deliverables alongside consultants and engineers who value rigour.

You should be pursuing or recently completed a degree in a quantitative field, comfortable with ambiguity, and able to explain results to non-technical stakeholders.

Qualifications

• Proficiency in Python for data work and ML libraries.
• Working knowledge of SQL and data cleaning, exploration, and visualization.
• Solid grounding in core ML concepts and model evaluation.
• Strong written English to communicate findings to non-technical stakeholders.

Responsibilities

• Prepare, clean, and explore datasets for modelling.
• Build and validate models, dashboards, or pipeline components.
• Explain analysis results and limitations to the team and leadership.

Skills

Python for data work

SQL

ML fundamentals

Data cleaning & EDA

Dashboard/visualisation

Strong written English

Education

Pursuing or recent graduate in Data Science / CS / Statistics / Engineering

Tools

pandas

NumPy

scikit-learn

Job description

Calibrax AI delivers end-to-end AI solutions, from strategy and model development to deployment, integration, and long-term optimisation. Our engineers build the systems that make AI actually work at scale: pipelines, APIs, integrations, and the infrastructure that keeps everything running. We work across a diverse range of clients and enterprises, in environments where reliability and performance are not optional.

We are looking for a Data Science Intern who can support our engineering and delivery teams in preparing, exploring, and modelling the data that feeds our internal systems and tooling. During this internship, you'll be working with real datasets, real constraints, and real deliverables, alongside consultants and engineers who expect you to bring rigour, not just curiosity.

Who You Are

Currently pursuing or recently completed a degree in Data Science, Computer Science, Statistics, Engineering, or a related quantitative field

Comfortable with ambiguity as client data can be messy, priorities shift, and you won't always get a clean brief

Genuinely interested in how AI systems behave in production

Able to explain your reasoning and your results to people who are not technical

Self-directed enough to move with minimal hand-holding, but knows when to flag a blocker early

Must Haves

Proficiency in Python for data work (pandas, numpy, and at least one ML library such as scikit-learn)

Working knowledge of SQL

Solid grounding in core ML concepts like regression, classification, clustering, and how to evaluate a model honestly

Experience with data cleaning, exploratory data analysis, and visualisation

Strong written English, since findings get communicated to the team and leadership, not just left in a notebook

What Success Looks Like

Within the first few weeks, you're pulling, cleaning, and validating datasets independently, with minimal supervision

You've contributed to at least one deliverable, a model, a dashboard, or a pipeline component

You can walk a non-technical internal stakeholder through your analysis and its limitations without oversimplifying or overselling it

You flag data quality or scope issues proactively, before they become someone else's problem