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Senior Data Scientist

New York, New York


Want to work on the largest dataset in talent acquisition? Join Radancy’s Data and Analytics team and tap into the biggest network of enterprise career sites, attracting more than 120 million applicants per year. Turn signals into valuable, actionable insights and conduct research, building on top of the most advanced analytics infrastructure in the industry. It’s the opportunity to build effective bidding algorithms and forecasting models that fundamentally transform recruitment advertising. Sound good? Let’s connect.

  • Full Time
  • Level: Mid-level
  • Travel: No

Success Profile

What makes a successful Data Analyst at Radancy?
Check out the traits we’re looking for and see if you have the right mix.

  • Collaborative
  • Detail-oriented
  • Efficient
  • Results-driven
  • Logical
  • Technologically savvy

Why Radancy

  • Healthcare

    Comprehensive coverage with flexible options, including FSA and HSA.

  • Flexible Time Off

    Holidays. Birthday. Me-days. Take the time you need.

  • Parental Leave

    6 weeks paid, so you can focus on what matters: bonding with your expanding family.

  • Autonomy

    Innovate, ask questions like “what if” and try new solutions without a fear of failure.

  • Collaboration

    Work closely with teams across departments and vendors to expand your skills.

  • Variety

    Global reach, wide client base and a breadth of product offerings – no two days will be the same.

Best company nyc 2022

Being able to learn new concepts and grow into a role is only one of the many advantages here. Working as a part of a collaborative team towards many different goals makes this job feel fresh every day.

Kevin, Data Analyst


Job ID 3536


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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