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Senior Data Scientist

New York, New York



Senior Data Scientist (TMP Worldwide Advertising & Communications, LLC (d/b/a Radancy), New York, NY)

  • Collaborate with Data Scientists on designing and implementing data related products and initiatives, including participation in requirements gathering, analysis, planning, and developing solutions.
  • Responsible for timely data availability and system up-time through support functions and in-depth testing.
  • Gather requirements, analyze, create design documents, and perform impact analysis.
  • Partner with Data Scientists and stakeholders in cultivating long-term strategic goals for development in conjunction with end users, managers, and clients.
  • Continuously evaluate industry trends for opportunities to utilize new technologies and data sources for improvement and prepare strategies to implement these enhancements in the data environment.
  • Research and recommend products, services, and standards in support of procurement and development efforts of the company’s data initiatives.
  • Develop an automated pipeline for the Data Scientists to be able to train, test, and deploy their models.
  • Produce guidelines and standards for the pipeline and the API’s that will be built.
  • Assist users with problems and resolve issues.
  • Create test plans, test cases, and test scripts and perform testing of the related environments.
  • Minimum Requirements:
  • Master’s degree or U.S. equivalent in Computer Science, Information Systems, Data Science, or a related quantitative field plus 2 years of progressively responsible post-graduate experience in each of the following:
    • Query development and design using SQL;
    • Working and deploying machine learning models from taking the data from the exploratory phase, to modeling, then validation, to production, and maintenance;
    • Using at least one of the following Python packages: Pandas, NumPy, or SciPy;
    • Using at least two of the following cloud providers: Google Cloud Platform, Amazon Web Services, or Azure;
    • Using at least one of the following machine learning Python packages: Keras, TensorFlow, Scikit-Learn, or PyTorch;
    • Using at least one of the following data pipeline Python packages: Luigi or Airflow; Building REST APIs with Flask or Django to deliver findings to users;
    • Using at least one of the following data visualization Python packages: Matplotlib, Bokeh, or Seaborn;
    • Building out Natural Language Processing (NLP) and/or Reinforcement Learning (RL) models; and
    • Deploying machine learning models across hybrid environments of on-premises and cloud providers.

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