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Senior Data Scientist

Sift

Sift

Data Science
San Francisco, CA, USA · Salt Lake City, UT, USA · Seattle, WA, USA
Posted on Friday, December 22, 2023

Our team helps customers proactively prevent payment fraud. Commerce and financial transactions are increasingly moving online. While the internet has enabled easier and instantaneous payments, it has also introduced a layer of anonymity between the transacting parties. To fully leverage the power of online commerce: there must be mutual trust between parties. Without trust, commerce cannot happen.

By providing a clear, accurate assessment of risk at multiple points of the end-user journey, we enable merchants to eliminate fraud losses, and create adaptive, frictionless experiences for low risk payments. We believe that machine learning is THE way to empower internet-scale businesses to prevent payment fraud.

What you’ll do

  • Research and analyze the gaps in our ML models (false positives and false negatives), observe and summarize fraudulent behavior patterns.

  • Define success metrics, and propose targeted model improvements so we can serve our customers better.

  • Collaborate with engineers on the team to build scalable and generalized ML models, ML features, or training regimes so we can prevent fraud at large scale with low latency.

  • Build systems that explain how a model arrived at a prediction.

  • Communicate effectively to influence stakeholders for buy-in.

  • Leverage anomaly detection algorithms to identify unusual behaviors for customer traffic patterns

What would make you a strong fit

  • Practical understanding of machine learning and data science concepts, and a track record of solving problems through rigorous ML paradigms.

  • Experience working with large datasets using Jupyter, Pandas, PySpark, PyTorch, Tensorflow or similar technologies.

  • Strong knowledge of statistics, experiment design, data science hypothesis and ML problem framing

  • Laser-focused on delivering customer value and prefer practicality over theoretical impact

  • Python coding and analytical tool building.

  • Strong communication & collaboration skills, and a belief that team output is more important than individual output

  • Degree in Statistics, Machine Learning, Computer Science, Applied Mathematics, Operations Research, or a related field

Bonus points

  • Experience working with scalable, real-time prediction systems in production

  • Familiarity with multiple machine learning or statistical packages in Python, R, MATLAB, or another programming language

  • Experience evaluating model performance

  • Advanced degree in Statistics, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, Operations Research, or a related field

A little about us:

Sift is the leading innovator in Digital Trust & Safety. Hundreds of disruptive, forward-thinking companies like 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

Let’s Build It Together

At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.

This document provides transparency around the way in which Sift handles personal data of job applicants: https://sift.com/recruitment-privacy