Senior Data Scientist at CB Insights
TITLE: Senior Data Scientist (CB Information Services, Inc. d.b.a. CB Insights – New York, NY)
HOURS: 40 hours per week, Monday-Friday, 9:00-5:00
DUTIES: Analyze how data science projects impact the business and design solutions accordingly. Spearhead use of best practice in using various machine learning and NLP techniques and technologies. Identify root causes and develop solutions to improve robustness for the data science teams systems. Drive improvement of code quality and serve as an example to follow through code reviews. Deliver complex large-scoped features independently, including designing and implementing a solution that is running successfully in production. Develop data models to effectively gather information from disparate sources, analyze it, identify trends, extract useful information and surface the information onto our system platform. Develop end-to-end machine learning and NLP-based systems to extract structured information from unstructured data. Identify key areas for workflow improvements and develop tools to help reduce time for development of these systems. Share machine learning and NLP expertise via presentations and knowledge sharing sessions. Collect business intelligence data from available industry reports, public information, field reports, and purchased sources. Build standardized data products to extract business intelligence of companies and industries which supports data driven decisions. Document and disseminate information regarding tools and the developed systems. Utilize best practices for training, testing, and validation to build accurate and reliable models. EOE.
REQTS: Must have Master’s degree or foreign equivalent in Computer Science, Quantitative Methods, Statistics, Economics, or a related quantitative field plus three (3) years of experience in the job offered, as a Data Scientist, or a related role. Must have three (3) years of experience with: using Machine Learning (ML) to design and implement data science solutions; analyzing business problems and the feasibility of a data science solution, data wrangling and visualization, and predictive modeling and fine tuning; developing Natural Language Processing (NLP) and Natural Language Generation (NLG) systems using industry best practices including information extraction, topic modeling, language modeling and linguistic surface realization; leveraging big data technologies and data warehouses including relational databases, Spark and Hadoop; statistical inference and modeling including hypothesis testing and model interpretations; experiment design and analysis; SQL; Python; version control systems and bash; defined and measured performance on machine learning systems for stakeholders; and documented deployed ML systems functionalities on the preservation of institutional knowledge.
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