Principal Associate, Data Scientist - AI for Data

Capital One Capital One · Banking · San Jose, CA +2

Data Scientist role focused on developing AI-enabled features across the data lifecycle within a fintech company. Responsibilities include building ML models through all phases, leveraging technologies like Python, AWS, and Spark, and collaborating with cross-functional teams. Requires experience with various ML techniques, deep learning frameworks, and NLP/IR/Search/Recommendations/Fine-tuning on LLMs.

What you'd actually do

  1. Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
  2. Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
  3. Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
  4. Flex your interpersonal skills to translate the complexity of your work into tangible business goals
  5. Move fast in an environment with ambiguity at times, and with competing priorities and deadlines.

Skills

Required

  • Bachelor's Degree in a quantitative field or equivalent experience
  • 5 years of experience performing data analytics
  • Python
  • SQL
  • Deep Learning Frameworks (TensorFlow, Pytorch)
  • Natural Language Processing
  • Information Retrieval
  • Search
  • Recommendations
  • Fine-tuning on LLM

Nice to have

  • Master's Degree in a quantitative field or MBA with quantitative concentration
  • PhD in a quantitative field
  • AWS
  • H2O
  • Spark
  • sentiment analysis
  • time series
  • deep learning

What the JD emphasized

  • large scale training and deployment of deep neural nets and/or transformer architectures
  • Natural Language Processing, Information Retrieval, Search, Recommendations, Fine-tuning on LLM on specific applications

Other signals

  • develop AI-enabled features
  • experiment, innovate and create next generation experiences powered by the latest emerging AI/ML technologies
  • Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
  • Iterate rapidly with researchers and engineers to improve a product experience while building the foundation of the platform.