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