Applied Scientist Intern

What you'll do

• Own the full machine learning lifecycle from data exploration and feature engineering to deployment and monitoring.
• Leverage Large Language Models (LLMs) to solve novel problems and create new product capabilities.
• Apply diverse techniques including deep learning, gradient boosting, and causal inference to business problems.
• Quantify the impact of work through A/B testing and other statistical methods.
• Translate complex business needs into scalable machine-learning-driven solutions for stakeholders.
• Partner with product and business leaders to turn model insights into actionable features.
• Write maintainable code using software engineering best practices like version control and testing.

What we're looking for

• Must be a student pursuing a B.S., M.S., or Ph.D. in a quantitative field with an expected graduation date between December 2027 and 2029.
• Graduate degrees are preferred.
• Must have strong mathematical foundations in machine learning, statistics, probability, and optimization.
• Must possess proficiency in Python and common data science libraries like pandas, scikit-learn, NumPy, and PyTorch.
• Must have experience wrangling data in a modern data warehouse such as Snowflake, BigQuery, Redshift, or Clickhouse.
• Must have a track record of curating datasets and building/evaluating machine learning models.
• Must be able to communicate complex concepts clearly to both technical and non-technical audiences.
• Relevant experience applying AI/ML, production ML mindset (Git, testing), and data orchestration experience are preferred.