обновлено 10 дней назад
Data Scientist (Logistics)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Data Scientist (Logistics): Building full-cycle analytics experiments, reports, and dashboards for a global logistics marketplace with an accent on quantitative analysis, experimentation, and actionable operational insights. Focus on solving ambiguous business problems, measuring marketplace levers, and using hypothesis testing, causal inference, and regression to guide cross-functional decisions.
Location: Sydney, NSW or Melbourne, VIC, Australia
Company
operates a global logistics platform connecting consumers, dashers, and businesses.
What you will do
- Partner with product, business, finance, and executive teams to guide strategic decisions.
- Build full-cycle analytics experiments, reports, and dashboards using SQL, Python, R, and statistical tools.
- Identify and measure business levers affecting essential metrics, revenue, growth, and marketplace efficiency.
- Apply statistical techniques, hypothesis testing, A/B testing, and other experimentation methods to validate findings.
- Generate insights into marketplace dynamics, user behavior, and long-term trends.
- Collaborate with strategy, operations, product, finance, and engineering while managing competing priorities.
Requirements
- 4+ years of experience in data analytics, consulting, or a related quantitative role.
- Strong ability to structure and solve ambiguous problems using hypothesis-driven, data-supported approaches.
- Experience owning strategic projects through completion with multiple cross-functional teams.
- Advanced SQL query skills, basic ETL knowledge, and experience with regression techniques.
- Proficiency in Python, R, or another programming language.
- Experience with analytics and data visualization tools, plus strong stakeholder management skills.
Culture & Benefits
- Work with a global Dasher & Logistics organization focused on faster, more affordable, and more reliable delivery.
- Contribute to logistics initiatives including routing, supply and demand, estimated arrival times, incentives, AI tools, autonomy, and drones.
- Drive real-world operational change through actionable data science rather than machine learning modeling alone.
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