What's the role?
We're seeking a Sr Data Scientist & Geospatial Data Analyst who combines the rigor of a Product Data Scientist with the investigative depth of a Last‑Mile Delivery Data Analyst. In this hybrid role, you will analyze geospatial and delivery data, build evaluation frameworks, influence routing and map content improvements, and shape product strategy through enterprise customer insights. You will join the Enterprise Solution product team and become the data detective who uncovers insights that drive our product forward. You'll dive deep into geospatial data, logistics planning and execution patterns, and system performance metrics to identify problems, spot opportunities, and guide algorithmic improvements. Your analysis will directly influence how our customers optimize last-mile planning and execution down to the last meter. This isn't just about generating reports—you'll be an integral part of the product development cycle, working closely with engineers and product managers to translate data insights into actionable improvements. You'll build the dashboards and monitoring systems that help us maintain quality, expand coverage, and continuously improve our delivery optimization algorithms. This role is ideal for a geospatially fluent data analyst with strong problem‑solving skills, capable of working with large, complex datasets and influencing product direction through clear insights.
What you'll do:
Last mile delivery and logistics optimization
1. Analyze delivery and sensor data to extract last meter location insights
2. Proactively identify coverage gaps and algorithmic problems before they impact customers.
3. Guide improvements to routing, location matching, and optimization models.
4. Conduct geospatial analyses to assess location accuracy and delivery efficiency.
Enterprise customer and map gap quality insights
5. Detect gaps, anomalies, and product issues using telemetry, API logs, customer signals, and usage data and translate insights into product improvements, KPIs, and roadmap recommendations.
6. Generate actionable insights from customer product usage and design data feedback loops to improve map coverage and content quality
7. Build scalable data pipelines and automated monitoring systems for continuous evaluation
Dashboards and communication
8. Build dashboards for map quality, delivery performance, and operational KPIs
9. Create automated reporting and alerting systems for critical metrics
10. Communicate insights clearly to technical and non‑technical stakeholdeers and drive strategic product decisions
Cross-functional collaboration
11. Work closely with Product, Engineering, Map Content, Data Engineering, and ML teams to integrate insights into product development.
12. Champion data‑driven decision‑making and data feedback loops
Who are you?
13. Master’s or PhD in Data Science, Computer Science, Geoinformatics, Statistics, or equivalent experience
14. 5+ years in data science, analytics, or geospatial analysis
15. Hands‑on experience with geospatial analysis tools and libraries (GeoPandas, PostGIS, Shapely, etc)
16. Strong skills in SQL, Python (pandas, numpy, matplotlib, seaborn, or similar libraries), and data engineering (Spark, ETL, Kafka)
17. Experience analyzing API logs, device telemetry, or anomaly detection
18. Demonstrated ability to identify patterns and anomalies in complex datasets and turn analytical findings into product improvements
19. Experience building dashboards and visualizations (Tableau, Grafana, Quicksight)
20. Experience with agentic AI tooling and motivation/interest to grow in this area
21. Strong analytical thinking with ability to translate data insights into product improvements
22. Excellent communication skills—able to explain technical findings to diverse audiences
23. Self-motivated with ability to proactively identify problems and propose solutions
Nice to have:
24. Background in mapping or GIS
25. Background in logistics optimization, routing algorithms, or operations research
26. Background in quality assurance or data validation in production systems
27. Experience with enterprise customer data or B2B ecosystems.
28. Experience working with ML models - performance evaluation, bias, clustering, outlier detection
29. Experience working with statistical analysis, A/B testing, real‑time monitoring, or data validation frameworks.
30. Exposure to Jupyter notebooks, version control (Git), and collaborative data analysis workflows
What do we offer?
31. A great work-life balance
32. Hybrid model of work
33. Challenging problems to solve
34. Collaborative and encouraging colleagues
35. Opportunities to learn, grow and develop
36. Regular feedbacks
37. Flexible working hours
38. Competitive salary plus bonus
39. Employee wellness programs and life-coaching sessions
40. Diverse team of fantastic & talented people from 60+ countries worldwide.