Data Scientist

Signifyd

We switched to a 4 day work week in January 2022 (32hrs / week)

Only considering candidates eligible to work in Budapest, Hungary ⚠️

The Data Science team builds production machine learning models that are the core of Signifyd's product.

We help businesses of all sizes minimize their fraud exposure and grow their sales. We also improve the e-commerce shopping experience for individuals by reducing the number of folks' orders that are incorrectly declined and by making account hijacking less profitable for criminals.

The team has end-to-end ownership of our decisioning engine, from research and development to online performance and risk management.

We value collaboration and team ownership -- no one should feel they're solving a hard problem alone.

Together we help each other develop our skills through peer review of experiments and code, group paper study to deepen our ML and stats understanding, and frequent knowledge-sharing through live demos, write-ups, and special cross-team projects.

The Data Science and Engineering teams at Signifyd have always had a strong contingent of remote folks, individual contributors and team leads. The challenges of working remotely aren't new to us and we strive to iteratively improve our remote culture.

How you'll have an impact:

  • Build production machine learning models that identify fraud
  • Write production and offline analytical code in Python
  • Work with distributed data pipelines
  • Communicate complex ideas to a variety of audiences
  • Collaborate with engineering teams to strengthen our machine learning platform

Past experience you'll need:

  • A degree in computer science or a comparable analytical field
  • 6+ years of post-undergrad work experience required
  • Building production ML models
  • Using visualizations to communicate analytical results to members outside your team
  • Hands-on statistical analysis with a solid fundamental understanding
  • Writing code and reviewing others' in a shared codebase, preferably in Python
  • Practical SQL knowledge
  • Designing experiments and collecting data
  • Familiarity with the Linux command line

Bonus points if you have:

  • Previous work in fraud, payments, or e-commerce
  • Data analysis in a distributed environment
  • Passion for writing well-tested production-grade code
  • A Master's Degree or PhD

Check out how Data Science is powering the new era of Ecommerce

Check out our Director of Data Science featured in Built In

#LI-Hybrid

Benefits:

  • Stock Options
  • Annual Performance Bonus or Commissions
  • Pension matched up to 3%
  • ‘Day one’ access to great health insurance scheme
  • Enhanced maternity and paternity leave (12 weeks full-pay for mums & dads)
  • Paid team social events
  • Mental wellbeing resources
  • Dedicated learning budget through Learnerbly

Signifyd's Applicant Privacy Notice

Signifydsignifyd.com

Optimize Revenue + Automate Orders + Eliminate Fraud

Working Week

We switched to a 4 day work week in January 2022 (32hrs / week)

  • Mon
  • Tue
  • Wed
  • Thu
  • 🏖️
    Fri

Our Vacation Policy

Our vacation policy varies by location e.g. in the USA we offer Discretionary Time Off Policy (unlimited) and in the UK we offer 20 days PTO plus public holidays. For the UK this works out as:

  • 28 days
  • 52 Fridays
  • 80 days off per year

Remote Working Policy

We have offices in San Jose, New York, Denver, Belfast, London, Sao Paulo and Mexico City. Many of us also work 100% remotely.

Company Benefits

  • Health insurance
  • 401K Match
  • Generous parental leave
  • Dentalcare
  • Equity / options
  • Equipment allowance

Our Team

We're a team of 550 across 19 departments:

  • engineering
    128
  • data science
    66
  • sales
    50
  • support
    44
  • operations
    24
  • business development
    22
  • marketing
    22
  • +12 more teams

Desirable Skills and Experience

  • Python
  • SQL
  • Machine learning
  • Data analysis
  • Statistical analysis
  • Visualization
  • Linux command line
  • Distributed data pipelines
  • Experiment design

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