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    Senior Data Scientist - Salt Lake City, United States - Sift Science

    Sift Science
    Sift Science Salt Lake City, United States

    3 weeks ago

    Default job background
    Description

    About our team:
    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


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


    A little about us:
    Sift is the AI-powered fraud platform securing digital trust for leading global businesses.

    Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly.

    Brands including DoorDash, Yelp, and Poshmark rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at and follow us on LinkedIn.
    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:
    #J-18808-Ljbffr
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