Northbeam

Data Scientist

Northbeam

full-time

Posted on:

Origin:  • 🇺🇸 United States • California

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Salary

💰 $125,000 - $150,000 per year

Job Level

JuniorMid-Level

Tech Stack

PythonSQL

About the role

  • Support both new and existing data science–powered products
  • Contribute to forecasting and experimentation capabilities, improving model accuracy and expanding measurement coverage
  • Explain statistical concepts to customers, troubleshoot measurement issues, and gather feedback to enhance Northbeam's product suite
  • Collaborate with engineering and product teams to translate customer needs into technical requirements
  • Provide technical expertise and support for non-technical stakeholders, both internal and external
  • Work directly with customers and internal teams to deliver reliable marketing measurement solutions
  • Flexibly shift between technical implementation and customer support as needs evolve
  • Grow through mentorship from senior team members and take ownership of technical projects

Requirements

  • Bachelor's degree (MS preferred) in Mathematics, Computer Science, or a highly quantitative STEM field
  • 2+ years experience in Data science and ML roles
  • Solid foundation in statistics, machine learning, and causal inference (examples: regression, time series analysis, A/B testing, Bayesian modeling, signal processing)
  • Proficiency in Python/R, SQL, and experience with data platforms and statistical modeling libraries
  • Experience with (or enthusiasm about) working in a small, fast-paced company, and desire to drive the implementation of projects start-to-finish
  • Strong growth mindset, desire to learn marketing measurement concepts
  • Comfort explaining technical concepts to non-technical audiences and troubleshooting customer issues
  • Bonus: Any experience in marketing attribution, MMM, experimentation, or related measurement challenges
  • Bonus: Experience developing and validating production models that drive real business decisions
  • Bonus: Familiarity with MLOps practices and data pipeline orchestration tools
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