Data Analyst

Melbourne
Tesla
Type: Full-time
Tesla
Level: Junior Level
Salary:
4 years ago
Deadline: 2020-10-25

Job Description

  • Work with stakeholders to take a vague problem statement, refine the scope of the analysis, and use the results to drive informed decisions
  • Write reproducible data analysis over petabytes of data using cutting-edge open source technologies
  • Understand and apply reliability concepts in your data analysis
  • Summarize and clearly communicate data analysis assumptions and results
  • Build data pipelines to promote your ad-hoc data analyses into production dashboards that engineers can rely on
  • Design and implement metrics, applications and tools that will enable engineers by allowing them to self-serve their data insights
  • Work with engineers to drive usage of your applications and tools
  • Write clean and tested code that can be maintained and extended by other software engineers
  • Operate and support your production applications
  • Keep up to date on relevant technologies and frameworks, and propose new ones that the team could leverage
  • Identify trends, invent new ways of looking at data, and get creative in order to drive improvements in both existing and future products

Job Requirement

  • Degree in Statistics, Econometrics, Computer Science, Physics, or a related quantitative field
  • Strong proficiency in Python, SQL
  • Deep statistical skills such as utilized in A/B testing, analyzing observational data, and modeling
  • Experience building data visualizations
  • Experience writing software in a professional environment
  • Experience with data science tools such as Pandas, Numpy, R, Matlab, Octave preferred
  • Experience building data pipelines preferred
  • Experience building web applications preferred
  • Experience building machine learning models in a professional environment preferred
  • Experience with continuous integration and continuous development preferred
  • Experience in devops, i.e. Linux, Ansible, Docker, Kubernetes preferred
  • Understanding of reliability concepts (Weibull, Lognormal, Exponential, etc.), life data (or survival) analysis, and reliability modeling preferred
  • Understanding of distributed computing, i.e. how HDFS, Spark and Presto work preferred
  • Proficient in Scala preferred

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