- Salary
- $2 – $5
- Location
- Kuala Lumpur - RedQ, Malaysia
- Type
- Full-time
- Department
- Education
- Education
- PhD
- Source
- Workday
Description
Job Description
YOUR ROLE AS A:
Data Scientist
WHAT YOU’LL CHAMPION:
You will Improve models and algorithms to further optimize business outcomes.
As a Data Scientist, you will work across the following areas:
Exploratory analysis: use data to suggest and prove hypotheses
Modeling: built optimization / predictive / statistical models to learn from data and estimate the unknowns.
Data operations: query data, deploy models and automate pipelines in cloud.
Experience with common data science toolkits, programming languages, visualisation tools and SQL/NoSQL databases.
Good applied statistical knowledge with emphasis in business and finance related statistical distributions, statistical testing, modeling, regression analysis, etc.
Experience with distributed computing platforms and open-source tools and libraries.
Familiar or prone to adopt design thinking methods.
Able to work under pressure and change, and balance among speed, reliability, interpretability.
Good working knowledge of productivity tools such as G Suite, Git, Jira, Confluence.
Experience with code versioning, code review and documentation.
WHO YOU ARE:
BS/MS/PhD in a Business, IT, Mathematics, Science or Engineering discipline
Up to 4 yrs relevant experience beyond first degree
Experience in one or more of the following specialized areas:
Machine Learning
2–5 years building production ML systems — beyond notebooks and Kaggle competitions.
Solid understanding of machine learning algorithms — XGBoost, LightGBM, neural networks, decision trees — with a clear grasp of why you tuned what you tuned.
Strong Python and hands-on experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
Demonstrable understanding of forecasting and regression pitfalls — lag feature leakage, target leakage in cross-validation, high-cardinality categorical handling, and the trade-offs between MAE, MAPE, and RMSE.
Ability to interpret models — SHAP, partial dependence, residual diagnostics — and explain results to non-technical stakeholders without dumbing them down.
Hands-on Google Cloud Platform experience, particularly BigQuery (window functions, partitioning, cost-aware SQL) and Vertex AI (training jobs, model registry, endpoints, pipelines).
Nice-to-have: deep learning for tabular and time-series problems (TFT, N-BEATS, NeuralProphet, TabPFN, Chronos); AutoML tooling such as PyCaret for rapid baselining.
Algorithm Engineering
Strong ability to implement, improve, and deploy ML and mathematical models in Python (Golang a plus for performance-critical services).
Experience productionizing models end-to-end — from SQL feature pipelines to deployed serving endpoints — on GCP using Vertex AI and BigQuery.
Conduct systems tests for security, performance, and availability of deployed models.
Develop and maintain design documentation, error analysis runbooks, and troubleshooting guides
Git-based workflows, CI/CD discipline, and code review hygiene
Monitoring discipline — drift detection, data quality checks, model performance tracking in production.
Nice-to-have: experience with LLM-based or agentic tooling (LangGraph, MCP servers, prompt engineering for structured outputs, eval harnesses for LLM systems).
WHERE YOU’LL GO:
Dispatcher to captain, ramp agent to data analyst, brand executive to CEO - these are some Dare To Dream stories of our Allstars.
WHAT YOU’LL ENJOY:
Physical Wellbeing: Key medical and insurance benefits, maternity expenses, flexible work arrangement, and health and fitness amenities.
Emotional Wellbeing: Paid time off, wellness programmes, and childcare amenities.
Financial Wellbeing: Resources relating to financial, personal skills and career growth programmes.
Allstars Specials: Free flights, unlimited discounted flights, and exclusive discounts with partners.
A unique Allstar culture like no other
