Назад
7 дней назад

Applied Scientist Intern (AI)

150 000$
Формат работы
hybrid
Тип работы
fulltime
Грейд
trainee
Страна
US
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Описание вакансии

TL;DR
Applied Scientist Intern (AI): Building, deploying, and evaluating end-to-end machine learning solutions for credit, fraud, growth, and spend management with an accent on LLMs, deep learning, causal inference, and statistical modeling. Focus on feature engineering, benchmarking, monitoring, A/B testing, and translating models into user-facing product capabilities.

Applied Scientist Intern

Company

Ramp

Conditions

6 days agoSalary: 150K - 150K

Skills

Candidate Availability

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

You will own an end-to-end machine learning project, from data exploration and feature engineering through training, benchmarking, deployment, and monitoring. You will apply LLMs, deep learning, gradient boosting, causal inference, A/B testing, and statistical methods to solve problems in credit, fraud, growth, or spend management. You will collaborate with product and business stakeholders to turn models and insights into user-facing features and strategy.

Requirements

  • Currently pursue a degree in Data Science, Computer Science, Math, Physics, Economics, Statistics, or another quantitative field with an expected graduation date between December 2027 and 2029
  • Understand machine learning, statistics, probability, and optimization
  • Have interest or experience integrating LLMs and agents into applied solutions
  • Use Python and data science libraries including pandas, scikit-learn, NumPy, and PyTorch
  • Use SQL to wrangle data in a modern data warehouse
  • Have experience curating datasets and building and evaluating machine learning models
  • Communicate complex concepts to technical and non-technical audiences

Responsibilities

  • Own the machine learning lifecycle from data exploration and feature engineering through training, benchmarking, deployment, and monitoring
  • Leverage large language models to solve novel problems and create product capabilities
  • Apply machine learning techniques including deep learning, gradient boosting, and causal inference
  • Quantify impact through A/B tests and statistical methods
  • Partner with product and business leaders to translate models and insights into strategy and user-facing features

Benefits

  • Apple MacBook
  • Catered lunches in the NYC office Monday through Friday
  • Weekly coffee stipend
  • Housing stipend

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