Data Scientist

San Francisco

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Posted 1 month ago

About the team

Our team researches and develops machine learning models and prototype systems that help make the Internet a safer place. We serve customers across multiple verticals such as online commerce, delivery service, finance, and travel sites etc. Our customers spread around the globe in both developed and developing countries. Our technology helps protect their users from ever-evolving online scams, payment fraud, abusive content, and account takeover, etc.  We are a forward-thinking team constantly challenging ourselves and the status quo to push the boundary of machine learning and data science across multiple product offerings at SIFT and collaborate with product engineering teams to deliver concrete customer value.

 We take pride in our work, not ourselves. Open and constructive feedback is valued and often required to ensure rigor in our work. We love learning, hacking, and sharing our findings. We believe that machine learning is THE way to empower internet-scale businesses to thrive.

What you'll do

  • Deep analysis of fraud patterns and customer goals, provide data-driven evidence to propose research initiatives
  • Evaluate cutting-edge machine learning technologies through literature survey and prototyping
  • Research and recommend modeling approaches based on customer value and technical risks 
  • Gather domain knowledge from stakeholders to define evaluation metrics and drive innovation towards customer goals
  • Address gaps in existing products and optimize model accuracy
  • Collaborate with engineering teams to understand system constraints, and propose architecture changes and product features to serve our customers
  • Collaborate with platform teams to build internal tools to support repeatable research process and help diagnosing model issues

What would make you a strong fit:

  • Deep knowledge of machine learning and data science best practices and hands-on practice of solving real-world problems through practical yet rigorous ML paradigms.
  • Experience working with large datasets using Jupyter, Pandas, PySpark, PyTorch, Tensorflow or similar technologies
  • You wrote and understand Java or other object-oriented programming languages
  • You love to perform deep analysis as well as hacking together functional prototypes
  • You are laser-focused on delivering customer value and prefer practicality over theoretical impact
  • You have a growth mindset, willing to mentor and learn from other data scientists and engineers
  • You tell compelling story through data, love ambiguity and feel comfortable to evaluate and manage competing research priorities
  • Excellent communication skills and collaborative work attitude

A little about us:

Sift is the leading innovator in Digital Trust & Safety.  Hundreds of disruptive, forward-thinking companies like Airbnb, Zillow, and Twitter trust Sift to deliver outstanding customer experience while preventing fraud and abuse.

The Sift engine powers Digital Trust & Safety by helping companies stop fraud before it happens. But it’s not just another anti-fraud platform: Sift enables businesses to tailor experiences to each customer according to the risk they pose. That means fraudsters experience friction, but honest users do not. By drawing on insights from our global network of customers, Sift allows businesses to scale, win, and thrive in the digital era.

Benefits and Perks:

  • Competitive total compensation package
  • 401k plan
  • Medical, dental and vision coverage
  • Wellness reimbursement
  • Education reimbursement
  • Flexible time off

Sift is an equal opportunity employer. We make better decisions as a business when we can harness diversity in our experience, data, and background. Sift is working toward building a team that represents the worldwide customers that we serve, inclusive of people from all walks of life who can bring their full selves to work every day.

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Job tags: Engineering Finance Java Machine Learning ML Pandas PySpark PyTorch Research TensorFlow Travel
Job region(s): North America