Job Description
Summary
Description
Develop Metrics: Design and implement metrics to measure the effectiveness and accuracy of models.
Failure Analysis: Perform detailed failure analysis to understand model weaknesses and identify areas for improvement.
Data Processing: Preprocess and clean large datasets to prepare them for modeling.
Model Optimization: Optimize models for performance and scalability, applying state-of-the-art techniques.
Collaborate: Work closely with cross-functional teams, including software engineers, product managers, and other data scientists, to integrate models into production.
Documentation: Document processes, model performance, and analysis results.
Minimum Qualifications
- Minimum requirement of a bachelors degree.
- Background in data science, machine learning, Computer vision and statistical data analysis
- Programming skills in data manipulation & processing (SQL & Python preferred)
Preferred Qualifications
- Demonstrated experience in in-depth analysis of machine learning model failures
- Experience crafting, conducting, analyzing, and interpreting experiments and investigations
- Proven expertise in data wrangling and developing data visualizations & reporting with toolings such as Tableau, Superset, AWS etc.
- Detail-oriented to keep track of and understand the workings of complex algorithms.
- Self-motivated and curious with creative and critical thinking capabilities to improve data quality evaluation methods for diverse and complex data annotation programs.
- Outstanding verbal and written communication skills, along with strong collaborative abilities.
- Familiar with machine learning interpretability methods.