Data Scientist

Quick Apply

Request a callback

Send To A Friend

Data Scientist

A Data Scientist is required to join a Product team on a permanent basis with our client - a FinTech (for good) startup taking the world by storm and changing the way we send money overseas.

The company enables money to be sent from smartphones to the mobile phones of family and friends, in over 140 countries to be paid out in cash, paid into a bank account or into a mobile money account.

Skills required of the Data Scientist:

  • Development experience in Python (or other similar / transferable scripting languages)
  • Good experience in SQL
  • Data warehousing knowledge / experience
  • Understanding of finance and behavioural trends
  • A strong interest in Analytics and a background in economics / analysis
  • Basic understanding of statistical analysis.
  • Preferred experience with a statistical package such as R, MATLAB, SPSS, SAS, Stata, etc.
  • Preferred experience with an Internet-based company.
  • Experience with large data sets and distributed computing (Hive/Hadoop)
  • BA/BS in Computer Science, Math, Physics, Engineering, Statistics or other technical field, advanced degrees preferred.

Key Responsibilities of the Data Scientist:

  • Provide an understanding or the customers behaviours, by applying analytical expertise in highlighting trends and patterns in the usages of the products.
  • Apply expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers and understand how our users interact with our products
  • Partner with Product and Engineering teams to solve problems and identify trends and opportunities
  • Inform, influence, support, and execute our product decisions and product launches.
  • The role will work in the below areas:
  • Data Infrastructure:
    • Designing business logic intermediary data schemas,
    • Understanding of how to maintain multiple data pipelines for company's big data platform
    • Building key data sets to empower operational and exploratory analysis
    • Automating analyse
  • Product Operations:
    • Designing and evaluating experiments monitoring key product metrics, understanding root causes of changes in metrics
    • Building and analysing dashboards and reports
  • Exploratory Analysis:
    • Help to influence the product roadmap by using insight
    • Understanding ecosystems, user behaviours, and long-term trend
    • Identifying levers to help move key metric
    • Evaluating and defining metric
    • Building models of user behaviours for analysis or to power production systems
  • Product Leadership:
    • Influencing product teams through presentation of work
    • Communicating of state of business, experiment results, etc to product teams
    • Spreading best practices to analytics and product teams