Data Scientist

September 14

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Smarsh

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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 • Methodology, results and insights reporting, including model Governance report drafting in collaboration with senior team members • Client/prospect guidance in machine learning model and analytic fine-tuning/development processes • 50% exploratory data analysis and annotation • 20% interaction with stakeholders to understand modeling needs • 30% data science experiments and model building

Requirements

• Bachelor’s degree in Computer Science, Applied Math, Statistics, or a scientific field • Approx. 2 to 5 years experience working with data & analytics (including school) • Experience working with Python • Familiarity with SQL and noSQL databases • Knowledge of “Big Data” frameworks like Hadoop, spark and Kafka are a plus • Familiarity with one or more data science and machine/deep learning frameworks and tooling, including scikit-learn, H2O, keras, pytorch, tensorflow, pandas, numpy, carot, tidyverse • Experience using Git, Linux/Unix, an IDEs • Master’s or Doctor of Philosophy degree in Computer Science, Applied Math, Statistics, or a scientific field (preferred) • Have 1+ years experience working with NLP, text analytics/classification (preferred) • Knowledge of NLP transfer learning, including word embedding models (gloVe, fastText, word2vec) and transformer models (Bert, SBert, HuggingFace, and GPT-x etc.) (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)