Job Description
JOB DESCRIPTION
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.
Experience mentoring or coaching junior analysts is a plus.
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