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Data Scientist en Sensortower

Sensortower · London, Reino Unido · Remote

Python

Ruby

Microsoft Excel

Software Engineer

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About the Role

Data Scientists at Sensor Tower are hybrid Data Scientists and Software Engineers. You would take data all the way from doing initial analysis to building data pipelines to final model implementation and monitoring. Data Scientists take full ownership of the back-end of the products they’re working on. We are looking for a hands-on thinker to help harvest new insights from our constantly growing foundation of quantitative information collected from the mobile app ecosystem. 


What You Will Work On
  • Prototype machine learning models in Python or Ruby.
  • Analyze query performance to ensure calculation efficiency.
  • Write tests for the implementation of machine learning models.
  • Collaborate with back-end engineers to understand the raw data being collected.
  • Collaborate with front-end engineers to create data visualizations for both external and internal customers.
  • Conduct ad-hoc data analysis based on requests from the Sales, CSM or Contents team.
  • Present results of various data analysis.



    • Experience We Are Looking For
  • Master’s degree or above in mathematics, statistics, or computer science.
  • 3+ years applied experience in business intelligence, data mining, analytics, or statistical modeling in technology or mobile industries OR 2+ years applied experience in data science in mobile market intelligence.
  • Ability to write code that is ready for production (Python and Ruby preferred).
  • Experience with adjusting data for bias.
  • Substantial experience with databases, querying data, and data structure manipulation.
  • Ability to communicate effectively with technical developers and non-technical marketing business partners.
  • Ability to produce rough timelines for deliveries plus solid understanding of steps necessary to complete a project.
  • Ability to come up with a rough project structure from scratch.
  • Ability to critically analyse given data, ask probing questions, and perform own research.
  • Substantial knowledge of statistical modeling techniques.
  • Mastery of one or more statistical visualization or graphing toolkits such as Excel, Jupyter Notebooks, or Google Spreadsheets.

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