对应专业:Analytics, Computer Science, Engineering, Information Technology
所需培训:N
培训时长:
所需培训领域:
前置经验:N
经验时长（月）:
替代领域学历:Y
替代专业:Information Systems, Mathematics, Physics, or a closely related field
可替代教育及经验组合:Y
所需教育层级:Master's
其他可替代教育层级:
认可经验年限:2
境外教育背景:Y
可替代职业:Y
可替代职业时长（月）:60
可替代职业名称:Job offered or developing supervised and unsupervised Machine Learning ML and CONTD IN H.14
是否为正常职业要求:N
是否需要外语:N
特殊技能:CONTD FROM H.10B Artificial Intelligence AI algorithms.br br Education and Experiencebr br Bachelors degree or foreign education equivalent in Analytics, Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and five 5 years of experience in the job offered or five 5 years of experience developing supervised and unsupervised Machine Learning ML and Artificial Intelligence AI algorithms.br br Or alternatively, Masters degree or foreign education equivalent in Analytics, Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and two 2 years of experience in the job offered or two 2 years of experience developing supervised and unsupervised Machine Learning ML and Artificial Intelligence AI algorithms.br br Skills and Knowledgebr br Candidate must also possessbr br Demonstrated Expertise DE performing advanced statistical analytics to develop and evaluate supervised and unsupervised ML algorithms Regression, Decision Trees, Neural Networks, linear and mixed effect models, Feature Selection, HyperParameter tuning, and ranking models; and developing and evaluating supervised and unsupervised ML algorithms, using Python and ML libraries PyTorch, scikitlearn, Tensorflow, and Keras.br br DE launching ML models in remote compute servers and in online environments to perform data and runtime profiling of solutions to assess the efficacy of ML and AI algorithms experimental design, AB testing, performance monitoring, AI fairness evaluation, and bias mitigations.br br DE writing productionlevel code to deploy AI solutions and to achieve greater runtime performance and low latency according to Test Driven Development mindset, using caching mechanism and clientserver architecture; prototyping and deploying ML solutions, using Dockercontainers and Cloudbased environments Amazon Web Services AWS; and building service endpoints, using REST API, Flask, and Dash.br br DE designing time series analysis frameworks that perform risk analysis and backtesting for models ARIMA, LSTM, ANN, Linear Regression, and Random Forest.