Urban Company Hiring Drive 2024 For Data Analyst | Location Gurugram

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Urban Company Hiring Drive 2024: Urban Company Hiring Drive 2024 for its Data Analyst. Urban Company Drive organizes off-campus drives for candidates in any batch. To apply, candidates should have a bachelor’s degree. if you are interested, please apply as soon as possible.

Company Name: Urban Company

Role: Data Analyst

Location: Gurugram

Experience: 0 – 2 years

Qualification: Bachelor’s degree

Batch: Any

Job Type: Full Time

Salary: 10-14 Lacs P.A.

Urban Company Hiring Drive 2024

Job Description:

  • As a Data Analyst, you will play a strong role in building a robust Data Warehouse and reporting layer in UC.
  • You will play a key part in standardizing and centralizing definitions across teams for various metrics.
  • Build Single source of definition, logic, computation, and documentation for each metric.
  • The objective is to build Analytics so that all reporting requirements are self-served.
  • As you grow you will also play a key role in building Insights & Data Science solutions for Key business segments including Supply Planning, Experience, Conversion, Predicting Request Loss Due to slot block, Designing Matchmaking System to make sure service professionals are well utilised etc.


  • Overall experience of 0-2 yrs. in Advanced Analytics/ Business Intelligence/Data Warehousing
  • Strong Background in Data Transformation Skills – SQL (Mandatory) & Python.
  • Strong focus on adding value to business and ability to take complete ownership.
  • Able to understand and align to long term vision of team
  • Should have curiosity to understand the core business problem behind the task assigned to them


  • Design the right schema to capture the most granular data.
  • Align the engineering and product teams on the Data Granularity and accuracy requirements.
  • Compute Single Source of truth for Key metrics
  • Build Alerts to detect bugs in the data pipeline.
  • Perform data analysis to test hypothesis and uncover factor influencing metric variations
  • Build Feature Store for Data Science implementations
  • Matching the right professional for each customer request (match making)
  • Quantifying how many requests were lost based on drop in conversion due to unavailable slots.
  • Use this data for onboarding providers at hyper local level (capacity planning)
  • Using various signals to find out fraud / disintermediation on the platform (Fraud detection)
  • Customer Segmentation.
  • Computing the state of the customer after each interaction.
  • Use customer segment for home screen personalisation and campaigns (personalisation)
  • Designing Match Making to make sure our professionals are well utilised (Provider Utilisation)
  • Identify customers, we should refund money easily whenever they face a bad experience.
  • Objective is to maximise LTV with minimum cost (Customer Retention)

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