Loại hình
Toàn thời gian
Hình thức
Tại văn phòng
Cấp bậc
Nhân viên
Ngành nghề
CNTT - Phần mềm
Mức lương
Thương lượng
Địa điểm
Quận 10, Hồ Chí Minh, Hồ Chí Minh
Tổng quan
- Set the direction for our modeling work — currently dynamic pricing and dispatching, with demand forecasting, matching and operational optimisation as likely next steps.
- Identify where machine learning and AI can measurably improve platform performance: pricing efficiency, dispatch quality, driver and customer experience, operational cost.
- Own delivery of the data group's projects: scope, prioritise, and drive them through to production alongside product and engineering.
- Make sure models reach production, are measured against real business impact, and stay operationally healthy — research that never ships does not count.
- Build an experimentation culture: design and evaluate A/B tests for pricing, product and algorithm changes.
- Keep the analytics the business relies on accurate and trusted, and use those insights to surface the next opportunity worth modeling.
- Lead, coach and grow the team; own hiring as it expands.
- Partner with Data Platform on data and feature requirements, and with Backend and Product Owners to ship model outputs into the product.
- 6+ years in data (data science, analytics, or a mix), including 2+ years managing a team.
- A track record of machine learning work that reached production and moved a business metric.
- Background in a data-heavy digital product; marketplace, logistics, fintech, e-commerce or ride-hailing preferred.
- Python for modeling and analysis (pandas, scikit-learn or equivalent), and strong SQL on a modern warehouse.
- Solid statistical foundations: experiment design, hypothesis testing, careful interpretation of results.
- Practical understanding of the full model lifecycle, from research through deployment, monitoring and retraining.
- Awareness of the current AI and ML landscape, and judgement about where it genuinely applies to a logistics business.
- Enough familiarity with warehouse architecture and analytical data modeling to work effectively with data and analytics engineers.
- Communicates complex findings simply and persuasively to non-technical audiences.
- Prioritises problems by business value, not technical interest.
- Comfortable operating with ambiguity in a fast-moving environment.
- Pricing, matching, dispatching or operational optimisation problems.
- Geospatial data, mapping or routing.
- Experimentation platforms, feature stores, or real-time inference.
- Basic Kubernetes knowledge and familiarity with ML workflow tooling (MLflow, Airflow, Kubeflow or similar).
- 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.
Quyền lợi
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