Operational Analytics & Insights
- Own end-to-end operational analytics: delivery performance, driver utilization, on-time rates, surge pricing effectiveness, and order pooling efficiency
- Conduct root cause analysis on operational anomalies e.g., why a specific zone has rising late delivery rates, whether surge pricing is effectively balancing supply and demand
- Build and maintain real-time operational dashboards that give operations teams visibility into live platform health and performance
- Design and implement alerting rules for critical operational metrics (e.g., abnormal surge trigger frequency, sudden driver supply drops, delivery SLA breaches)
- Partner with Product and Operations teams to translate operational data into actionable recommendations
ML Model Evaluation & Business Impact
- Define and track business metrics to evaluate the impact of ML models (surge pricing, ETA prediction, order pooling) on operational KPIs
- Design and analyze A/B tests and experiments for model rollouts
- Collaborate with ML Engineer to establish model performance baselines and monitor business-level model health
- Bridge the gap between model accuracy metrics and real-world business outcomes
Requirements
- 4+ years of experience in data analytics, with at least 2 years focused on operational or product analytics in a tech/logistics/marketplace environment
- Strong SQL skills (complex queries, window functions, CTEs, query optimization) this is your daily tool
- Proficiency in Python (pandas, numpy) for data manipulation and ad-hoc analysis
- Experience building dashboards and reports in BI tools (Metabase, Tableau, Looker, or similar)
- Experience working with Databricks or similar lakehouse/data warehouse platforms
- Demonstrated ability to go beyond what happened to why it happened and what should we do about it
- Strong communication skills ability to present findings and recommendations to both technical and non-technical stakeholders
- Experience designing and analyzing A/B tests
Nice to Have
- Experience in logistics, ride-hailing, delivery, or marketplace domains
- Exposure to real-time/streaming data and real-time dashboards
- Familiarity with ML model evaluation from a business perspective (precision/recall tradeoffs, model monitoring)
- Prior experience mentoring junior analysts or leading a small analytics team
- Familiarity with dbt, Airflow, or similar data transformation/orchestration tools
- Strong stakeholder management skills, with the ability to effectively collaborate with and present to senior leadership.
- Self-driven learner, quick to acquire and apply new knowledge and technical skills.
