*All applicants must be based within the U.S*

What we’re looking for

Responsibilities:

• Contribute to our codebase with product innovations and utility functions, ensuring continuous improvement of our data science processes
• Lead quality assurance efforts to ensure high quality data flows into our machine learning models
• Collaborate with Customer Success and Product teams to gain insights into user needs and provide educational support regarding data-driven solutions
• Solve modeling-related issues, tackle bugs, and address technical debt within our machine learning infrastructure
• Work autonomously in a smaller team environment, handling end-to-end project responsibilities
• Follow and adhere to group’s standards such as SLAs, code styles, and deployment processes
• Assist the team in monitoring pipeline health via observability tools and metrics
• Participate in refactoring efforts as platform application needs evolve over time

Competencies:

• Bachelor’s degree or higher in Data Science, Statistics, Mathematics or other hard sciences
• >= 1-2 years of experience in a Data Science industry role or an advanced degree from an appropriate field
• Tech Stack Requirements: Must have expertise in Python coding, SQL, data visualization tools, and (preferred) some experience with Pandas
• Experience building ML applications and interpreting associating output, including use of regression-based methods
• Exposure to working cross-functionally with other teams such as Product, Customer Success, and/or Data Engineering
• Experience connected to marketing and/or marketing mix models (mmm)
• Comfort with an array of statistical concepts and methods; (nice to have) experience with Bayesian reasoning and/or causal inference

Benefits:

• Competitive salary with ability to evolve your role for new compensation packages
• Company equity
• 401k retirement plan + company match
• Health insurance 99% covered (+family)
• $100 monthly for personal happiness
• Family leave time (includes Paw-ternity leave for furry loved ones)
• Learning budget for relevant courses of interests