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Data Scientist Job Wave

IT Jobs. Wave Jobs

  • You can work remotely from anywhere (between UTC -5 and +2) with reliable Internet access.
  • Wave covers travel once per year to one of our operating countries in Africa, as well as a yearly stipend of $1,200 to meet with coworkers.
  • Our salaries are competitive and are calculated using a transparent formula. For this role, depending on your level and location, we offer a salary of up to $149,200 USD (paid in your local currency equivalent), plus a generous equity package.

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  • Subsidized health insurance for you and your dependents and retirement contributions (both vary from country to country).
  • 6 months of fully paid parental leave and subsidized fertility assistance.
  • Flexible vacation, with most folks taking between 21-30 days exclusive of statutory holidays.
  • $10,000 annual charitable donation matching.

Education and Experience

  • Minimum Bachelor’s degree in a quantitative field such as Statistics, Mathematics, Economics, Computer Science, Engineering, or a related discipline. A Master’s or PhD is a plus.
  • 4+ years experience in applied data science or similar experience.
  • Demonstrated experience with data analysis, machine learning and product A/B tests.
  • Experience in using advanced analytics for optimising targeting, rewards or incentive programs is a big plus.

Technical skills

  • Proficiency in applying machine learning methods to solve business problems.
  • Very strong Python skills with expertise in data manipulation, analysis and machine learning. Must be comfortable writing code that runs in production.
  • Competent in SQL.
  • Solid foundation in probability and statistics.
  • Thrive on spotting key problems and finding the fastest, most effective solutions.
  • Enjoy and excel at working on machine learning and statistics problems.
  • Quickly build and validate proof-of-concept solutions, refining based on real-world feedback.
  • Prefer pragmatic approaches but apply complex methods when the impact is worth it.
  • Work iteratively, knowing when a project is “good enough” to avoid over-polishing.
  • Communicate insights clearly to all audiences and make confident, data-driven recommendations.

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