Palo Alto, CA
Landing AI – Engineering
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.