Applied ML Research Scientist

Boston, MA

Who We Are:

Our mission at Twitter Cortex Applied Research is to empower and guide Twitter’s product teams to solve customer problems using Machine Learning best practices. We use machine learning (deep learning and traditional methods), statistical analysis, clustering, time series modeling, and many other analytical techniques to process very large-scale datasets. We do research, build prototypes, prove their offline effectiveness, and work together with other teams to push them to production. Here are some public examples of recent work at Twitter related to what we work on:

What You’ll Do:

Because our team's purpose is to discover creative solutions to Twitter's most important and difficult challenges, you will get to work on high impact projects utilizing Machine Learning to improve the product at a large scale:

  • Research user behavior at scale, ranging from individual-level attributes to user populations in order to inform the range of ML solutions we’d like to apply
  • Apply advanced statistical and machine learning techniques to model user behavior, identify causal impact and attribution, and build and benchmark metrics
  • Conduct studies to learn from our vast amounts of data, including exploratory data analysis, investigating A/B test results, observational and network data, etc. 
  • Use tools for interacting with large datasets such as Scalding, Spark, and Presto
  • Work closely with our ML and software engineers to develop new models
  • Use Python to conduct complex data processing
  • Use data visualization tools (e.g. Tableau or Zeppelin)

Who You Are:

  • Experience doing research in applied ML
  • Good understanding of how to apply modeling to product needs
  • Comfort with digging into new problem domains, applying a wide variety of tools for diverse problems
  • Effective stakeholder communication, turning data findings into actionable insights that can direct our modeling efforts
  • Self-starter who can own complex projects from start to finish


  • 3+ years professional related work experience (not including internships) or completed a PhD in a quantitative field and professional experience
  • Hands-on proficiency with at least one data science programming language, with preference to Python over R
  • Experience coding in Java, Scala, or C++
  • Firm grasp of CS fundamentals, Data structures, and algorithms
  • Experience analysing large scale quantitative customer data to solve problems and answer questions
  • Experience in building ML models 
  • Experience with data visualization
  • Experience with social network data a plus

Hiring Process

Step 1

After you apply, a recruiter may reach out to you for an introductory call.

Step 2

If your background is a match for the role, you may phone interview with 1-2 people.

Step 3

If you continue through the process, you will come onsite 1-2 times to interview with a total of 5-10 people.


Personal Information

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