Wheelseye - Business Analyst - Part Truck Load

WheelsEye is a hyper-growth tech logistics startup. We started in 2017 with the idea to empower the Truck Fleet Owners, and enable them and their business via technology.

We started off as a GPS tracking company and today we have created an entire ecosystem of services aimed at transforming truck logistics in India. We are providing fleet owners with a one-stop platform for all their needs: Fastag, Fuel, Credit, Insurance, Maintenance, Marketplace, and more.

We are in "Mission Mode". We operate with only one intent - adding value to a truck owner's life and work, solving our customer's problems, and adding value to their lives. Our constant endeavor is to create and evolve data-driven software solutions that conquer logistic business problems and inspire more thoughtful data-driven decision-making.

We are a bunch of passionate and hungry individuals who are on the road to building a better India. Problem Solving and Ownership are at the core of what we do; having the right intent & focus shall help us achieve the same.

Roles and responsibilities:

- Through the right data which can be accessed quickly & through the right Infrastructure.

- Help them discover patterns around critical dimensions & user segments

- Ensure data accuracy by validating data for new and existing products

- Understand product flows, and funnels & identify the leakages/drop points for different types of user personas & accordingly suggest recommendations

Core skills:

- Hands-on experience in any BI visualisation tool (Tableau, QlikSense, Looker, Superset, Power BI etc)

- Excellent SQL skills and basic to intermediatory experience in Python scripting language

- Hands-on experience with Excel & Googlesheet

- Analytical Thinking: Outstanding analytical and problem-solving skills. ability to break the problem statement during execution

- Technical Experience (Good to have) : Knowledge of Google Analytics, BigQuery & Mixpanel, A/B testing & events writing experience and understanding of statistical methods and experiment designs, regression techniques.