Data Scientist

Palo Alto, CA
Landing AI – Engineering
Full time
Conduct data science research including utilizing probabilistic graphical models (PGMs) and Pearson correlation to analyze how each feature in machine learning models impacts the final prediction outcome.
Conduct quantitative performance evaluation on models using mean average precision (mAP), F-beta-scores and confusion matrices.
Use knowledge of and experience with modern data science and machine learning models including Convolutional Neural Network architectures, Gaussian processes, Random Forests, Support Vector Machines.
Build natural language processing models such as Transformer Networks using pre-training methods like XLNet and BERT for sentiment analysis to improve marketing strategy.
Utilize Convex Optimization and libraries such as CVXPY for particle tracking applications.
Master degree in Statistics, Mathematics or related fields plus one year experience in the job offer or 1 year experience as a Machine Learning Statistical Modeling Engineer or related occupation.
Requires 1 year of experience using knowledge of and experience with deep learning frameworks including TensorFlow, Pytorch, and Keras to build machine learning and statistical models; using knowledge of and experience with machine learning training experiments on high powered NVIDIA GPU machines and deploy models with Docker using AWS Elastic Container Service (ECS); using knowledge of and experience with Python or Javascript to implement and maintain medium to large scale software projects (10,000+ lines of code); using knowledge of and experience with data and statistics tools including Pandas, Numpy, Scikit-Learn, and Plotly for tasks including data processing, data visualization; Using knowledge of and experience with web development frameworks including React front-end, Django back-end, Jenkins continuous integration, and SQL database.