Research Engineer - III

3 days ago

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Smarsh

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Smarsh enables organizations to manage the risk and uncover the value within their communications data.

cloud-based solutions • data-leak prevention • risk-based surveillance • information governance solutions • digital communications compliance

1001 - 5000

💰 Private Equity Round on 2016-01

Description

• Development of machine learning models and other analytics following established workflows, while also looking for optimization and improvement opportunities • Data annotation and quality review • Exploratory data analysis and model fail state analysis • Attend and actively participate in planning, stand-ups, retros, and other team meetings and discussions • Contribute to Research discussions • 70% Machine Learning model development and evaluation • 20% interaction with stakeholders to understand modeling needs • 10% Machine Learning model support

Requirements

• Bachelor’s degree in Computer Science, Applied Math, Statistics, or a scientific field with research emphasis • 1+ year research experience working with data & analytics (including school) • Experience working with Python • Familiarity with one or more data science and machine/deep learning frameworks and tooling, including scikit-learn, H2O, keras, pytorch, tensorflow, pandas, numpy, carot, lightning, Nvidia NeMo • Experience using Git, Linux/Unix, IDEs • Master’s or Doctor of Philosophy degree in Computer Science, Applied Math, Statistics, or a scientific field (preferred) • 1+ year experience working with NLP, text analytics/classification, audio analytics (preferred) • Knowledge of transfer learning, including word embedding models (gloVe, fastText, word2vec) and transformer models (Bert, SBert, and GPT-x etc.) (preferred) • Experience in using various Large Language Models (LLM) (preferred) • Experience with natural language processing toolkits like NLTK, spaCy (preferred) • Knowledge of microservices architecture and continuous delivery concepts in machine learning and related technologies such as helm, Docker and Kubernetes (preferred) • Cloud computing (AWS, GCS, Azure) (preferred)