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Hybrid Data Scientist (INDG)

Bloomberg Industry Group · Arlington, United States Of America · Hybrid

  • Senior
  • Office in Arlington
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You are a research scientist and engineer who wants to work in the areas of machine learning, natural language processing, information extraction, graphical models, summarization, information retrieval, recommend systems, and/or knowledge graphs. You will bring new ideas, evangelizing them, and shepherding their adoption within the team. You extract and identify relevant, meaningful, and actionable information from structured and unstructured data in real-time and provide advanced ways of accessing this data (such as search, summarization, and recommendations).

What you will do:

  • Proactively explore, research, describe, and analyze data. 

  • Develop tools and strategies related to text analytics, machine learning, and semantic enrichment 

  • Develop and maintain strong working relationships across departments and with key partners and stakeholders. 

  • Be tuned in to market trends and emerging technologies to find innovative solutions. 

  • Learn cutting-edge research in advanced ML & NLP topics and devise an efficient application for projects. 

  • Take ownership of technical solutions and direct machine learning strategy for the team. 

  • Drive, design, and develop projects as the principal point-of-contact, with the ability to determine suitable ML models, direct feature engineering processes and negotiate KPIs per business needs. 

  • Participates in special projects and performs other duties as assigned. 

You need to have:

  • Bachelor’s degree in related field or equivalent experience. 

  • 5 years of experience working with large data sets and managing data projects. 

  • Experience with programming in Java, Python, SQL. 

  • Experience with distributed computational frameworks (YARN, Spark, Hadoop, Kubernetes, Docker), cloud-based computingApache Solr, Lucene, or Elasticsearch). 

  • Experience with managing diverse data sources, including preprocessing, cleansing, and verifying the integrity of data. 

  • Experience with working with state, federal legislative or regulatory data is a plus. 

  • Familiarity with SQL and NoSQL data modeling and exploratory data visualization, preferred. 

  • Knowledge of advanced concepts such as weakly supervised learning, reinforcement learning, and deep learning, preferred. 

  • Extensive knowledge of leading open source data analysis tools, ML libraries, and NLP processing techniques (including topic modeling and summarization). 

  • Knowledge of descriptive and inferential statistics, regression, multivariate and univariate hypothesis testing. 

  • Excellent understanding of ML algorithms (e.g. KNN, Naive Bayes, decision trees, ensemble models, clustering) and the ability to select best methods for a business problem. 

  • Ability to analyze and manipulate unstructured and semi-structured data (text, PDF, JSON and XML files). 

  • Experience working with REST APIs (actual REST API development is a big plus) 

Equal Opportunity


Bloomberg Industry Group maintains a continuing policy of non-discrimination in employment. It is Bloomberg Industry Group’s policy to provide equal opportunity and access for all persons, and the Company is committed to attracting, retaining, developing, and promoting the most qualified individuals without regard to age, ancestry, color, gender identity or expression, genetic predisposition or carrier status, marital status, national or ethnic origin, race, religion or belief, sex, sexual orientation, sexual and other reproductive health decisions, parental or caring status, physical or mental disability, pregnancy or maternity/parental leave, protected veteran status, status as a victim of domestic violence, or any other classification protected by applicable law (“Protected Characteristic”). Bloomberg prohibits treating applicants or employees less favorably in connection with the terms and conditions of employment, in all phases of the employment process, because of one or more Protected Characteristics (“Discrimination”).