Staff Data Scientist, Hungary

Job Description

Signifyd is seeking a Staff Data Scientist to join their team in Budapest, Hungary. The Senior Analyst will be part of the Merchant Solutions team within the Data Science organization and will be responsible for building, maintaining, and monitoring production ML models and risk management tools. The ideal candidate will have a strong background in computer science or a comparable analytical field, with 7+ years of post-undergrad work experience.

Signifyd values collaboration and team ownership, encouraging team members to influence the direction of travel and contribute to improving the e-commerce shopping experience.

Responsibilities:

• Build production machine learning models to identify fraud
• Collaborate with engineering teams to strengthen the machine learning platform
• Own and ship multiple large products, complex libraries, or major pieces of software
• Deepen the understanding of key entities used in decisions
• Write production and offline analytics code in Python
• Communicate complex ideas to a variety of audiences
• Lead a wide range of complex situations across multiple axes: scale, uncertainty, and interconnectedness

Requirements:

• A degree in computer science or a comparable analytical field
• 7+ 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

The job offer includes:

• 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