Microsoft-posted about 1 year ago
$127,200 - $208,800/Yr
Full-time • Mid Level
Redmond, WA
10,001+ employees
Publishing Industries

The Data & Applied Scientist II role at Microsoft focuses on leveraging advanced computational intelligence to tackle challenges in the online advertising industry. The position involves building and maintaining production-level machine learning models to optimize ad relevance, utilizing cutting-edge technologies such as data mining, machine learning, and natural language processing. The successful candidate will work within a dynamic team to enhance user experience and advertiser ROI, contributing to the development of impactful solutions in a rapidly growing field.

  • Leverage understanding of data science and business to examine projects and evaluate project plans for resources, risks, and requirements.
  • Build and maintain production-level machine learning models to assess and predict ad relevance based on user contexts.
  • Utilize Python, PyTorch, and open-source libraries to train and fine-tune large language models.
  • Apply advanced techniques like transfer learning and prompt engineering to tailor pre-trained models for advertising scenarios.
  • Explore data for key attributes and contribute to data quality reports.
  • Collaborate with others to perform data-science experiments using established methodologies and statistics.
  • Derive meaningful insights from massive datasets using machine learning and statistical modeling techniques.
  • Communicate findings and insights effectively to diverse audiences and stakeholders.
  • Manage and manipulate petabyte-scale datasets using various tools and programming languages.
  • Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field.
  • OR Master's Degree in a related field with 1+ years of data-science experience.
  • OR Bachelor's Degree in a related field with 2+ years of data-science experience.
  • Proficiency in programming languages such as Python, R, C#, C++, Java, and SQL.
  • Experience with machine learning solutions and algorithms.
  • Experience with Large Language Models (LLMs) such as GPT or BERT.
  • In-depth knowledge of natural language processing (NLP) techniques.
  • Familiarity with multi-modal models like ViT and CLIP.
  • Ability to work independently and collaboratively in a team environment.
  • Understanding of state-of-the-art machine learning and deep learning technologies.
  • Health insurance
  • 401k
  • Paid holidays
  • Flexible scheduling
  • Professional development
  • Tuition reimbursement
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