Offers “Amazon”

Expires soon Amazon

Data Scientist

  • USA
  • Accounting / Management control

Job description


The Science team drives evidence-based decision-making throughout Twitch. We are researchers and leaders who advise product and business decisions and identify ways to use data and models to develop new capabilities. We are an interdisciplinary team leveraging experts in analytics, data science, and user and audience research to improve the world's leading livestreaming platform.

Our team seeks an outstanding data scientist with demonstrated experience delivering insights from complex data. We prize creative problem solvers with the ability to draw on an expansive methodological toolkit to understand the unique Twitch environment. Our problems include attributing values to actions in complex, multi-sided markets and helping diverse creators discover their drivers of success. The ideal candidate combines great intuition with data-science acumen to grapple with these and other challenges and guide decision-making at the highest levels of Twitch.

· Drive Twitch's knowledge of users and creators through ownership of research projects from data collection to model development, estimation, and validation
· Balance short-term projects with long-term, foundational and/or transformative research
· Autonomously identify and pursue research with significant business impact, and make compelling cases for prioritization and resource allocation
· Collaborate with specialists in data science, analytics, engineering, and economics disciplines to efficiently develop reliable and reproducible analyses at scale
· Deliver well-documented datasets, tools, and reports to key technical and business stakeholders

Ideal candidate profile


· PhD-level training in computer science, statistics, or highly quantitative field OR an MS with 2+ years experience on a data, research, or applied science team OR equivalent applied research experience
· Excellent written and verbal communication skills on quantitative topics
· Expertise in applying supervised and unsupervised algorithms to large-scale data
· Fluency in data-science fundamentals: data manipulation in R or Python, SQL, and statistics
· Keen judgment for balancing research depth and pacing