Data Scientist Ii, Amazon Currency Convertor

Amazon Amazon · Big Tech · Seattle, WA · Data Science

Data Scientist II at Amazon Payments focused on building analytical solutions for the Amazon Currency Convertor using Gen AI, LLM, and other machine learning techniques for text analytics, segmentation, and prediction. Responsibilities include applying causal inference, developing descriptive and predictive solutions, collaborating with stakeholders, innovating with modeling techniques, performing exploratory data analysis, and building models using standard techniques. Specific tasks involve fine-tuning Amazon LLMs for text summarization, preventing catastrophic forgetting, feature engineering, and implementing data flow solutions.

What you'd actually do

  1. Understand the applications of causal inference models on real datasets, including assessment of marketing campaigns, online experiments, uplift analysis etc
  2. Understand the business reality behind large sets of data and develop meaningful solutions comprising of analytics as well as marketing management
  3. Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus are
  4. Innovate by adapting new modeling techniques and procedures
  5. Effective exploratory data analysis, and model building using industry standard regression and classification techniques such as Random Forest, XGBoost package, Keras framework
  6. Demonstrate thorough technical knowledge Fine Tuning of Amazon LLMs to handle large blocks of text, using Generative AI to solve for summarization tasks and prevent catastrophic forgetting, feature engineering of massive datasets,
  7. Be passionate about working with huge data sets and be someone who loves to bring datasets together to answer business questions. You should have deep expertise in creation and management of datasets
  8. Have exposure at implementing and operating stable, scalable data flow solutions from production systems into end-user facing applications/reports. These solutions will be fault tolerant, self-healing and adaptive

Skills

Required

  • 2+ years of data scientist experience
  • 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
  • 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
  • Experience applying theoretical models in an applied environment
  • Python
  • Perl
  • scripting language
  • ML or data scientist role with a large technology company

What the JD emphasized

  • Fine Tuning of Amazon LLMs
  • Generative AI
  • LLM

Other signals

  • Gen AI
  • LLM
  • machine learning techniques
  • text analytics
  • segmentation
  • prediction
  • causal inference models
  • online experiments
  • uplift analysis
  • regression
  • classification techniques
  • Random Forest
  • XGBoost
  • Keras
  • Fine Tuning of Amazon LLMs
  • Generative AI
  • summarization tasks
  • feature engineering
  • data flow solutions