• Build machine learning models end-to-end, from problem framing, data exploration, feature engineering, training, evaluation, deployment support, to post-launch monitoring.
• Analyze large-scale operational and behavioral datasets to identify patterns, risks, opportunities, and modelable signals.
• Define offline evaluation metrics, experiment design, and success criteria that reflect both model performance and business impact.
• Work with ML Engineers, Data Engineers, Backend Engineers, Product, and business teams to bring models into real-world usage.
• Monitor model performance after launch and propose improvements when data patterns, user behavior, or business conditions change.
• Identify and mitigate modeling risks such as data leakage, sample bias, label bias, data drift, cold start, feedback loops, and train/serve mismatch.

• Strong foundation in statistics, machine learning, and data analysis.
• Proficiency in Python and common ML/data libraries such as pandas, NumPy, scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.
• Strong SQL skills and ability to work with large-scale structured datasets.
• Experience with feature engineering, model validation, experiment design, and performance evaluation.
• Ability to translate ambiguous business problems into clear hypotheses, model objectives, and measurable metrics.
• Good communication skills, with the ability to explain model behavior, trade-offs, and data insights to both technical and non-technical stakeholders.
• Comfortable working beyond notebooks and supporting models through production usage, monitoring, maintenance, and improvement.

Nice to have

• Experience in marketplace, logistics, mobility, fintech, risk, pricing, recommendation, dispatching, or real-time decision systems.
• Experience with causal inference, optimization, uplift modeling, time-series forecasting, ranking, graph-based modeling, or reinforcement learning.
• Familiarity with A/B testing, backtesting, online evaluation, model monitoring, retraining, or MLOps workflows.
• Experience working with production data pipelines, feature stores, model registries, or cloud platforms.

• Physical Wellbeing Benefit: General Insurance, Medical check-up, Accident Insurance, Healthcare Insurance
• Emotional Wellbeing Benefit: Company Trip, Year End Party, Aha Hour Activities, Special Day Gifts, Aha Club (Badminton, Soccer).
• Financial Wellbeing Benefit: Grab/Be For Work (Tech/Lead Level), Workplace Relocation, 13th Month Salary, PP Appreciate, Annual Leave Remain.