Data Scientist II

🌍 УдалённоMiddleПолная занятость🌐 Глобал

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Хорошие условия

Навык востребован (python), за такой навык на рынке платят больше. Но вилки нет, про деньги придётся договариваться с нуля.

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Чем предстоит заниматься

Partner with the Business Unit Lead and their merchant portfolio to identify gaps in decisioning performance and implement solutions, with guidance from senior team members

Utilize existing, or build net new production machine learning models that identify fraud, in collaboration with other data scientists and machine learning engineers

Identify and build automation that reduces repetitive manual work

Run experiments to identify optimal decisioning strategies, balancing complexity and performance

Communicate complex ideas to a variety of audiences, from Customer Success and Sales, to limited interactions with external customers

Write production and offline analytical code in Python

Work with distributed data pipelines in Spark/Databricks/GCP

Наши требования

A degree in computer science or a comparable analytical field

3+ years of post-undergrad work experience required

Strong verbal and written communication skills

Strong machine learning and statistical background

Write code and review others' in a shared codebase in Python

Practical SQL knowledge

Design experiments and collect data

Experience with distributed analytics and data tooling such as Spark and Databricks

This role has on-call shifts, as part of our weekend rotation, Fri/Sat/Sun. While the number of shifts is subject to change, currently it works out to about six weekends a year

Previous work in fraud, payments, or e-commerce

Data analysis in a distributed environment

A passion for writing well-tested production-grade code

Experience with AI coding agents and automation

Experience of running A/B tests in production environments

An advanced degree

Stock Options

Annual Performance Bonus or Commissions

Pension matched up to 8%

‘Day one’ access to great health, dental and optical insurance scheme

Generous annual leave plus public holidays

Cycle to Work Scheme

Enhanced maternity and paternity leave (12 weeks full-pay for mums & dads, plus 12 weeks half-pay for mums)

Regular paid social events organized by our social committee

Mental wellbeing resources

Dedicated learning budget through Learnerbly

Дополнительно

At Signifyd, we help merchants confidently grow their businesses by building trusted relationships with their customers. Our advanced technology, combined with a team genuinely invested in our clients’ success, creates frictionless shopping experiences, approving more good orders, protecting revenue, and keeping customers happy

Trusted by thousands of leading merchants across more than 100 countries, we securely process billions of transactions each year. Our people are the heart of everything we do, driving our mission forward with commitment, empathy, and creativity. Join us on our mission to empower confident, fraud-free commerce by helping online retailers provide superior customer experiences and eliminate fraud

The Applied Decision Science (ADS) team builds production ML models and risk management tools that are the core of Signifyd's product. We help businesses of all sizes minimize their fraud exposure and grow their sales. We improve the e-commerce shopping experience for everyone by reducing the friction experienced by good buyers and blocking fraudulent purchase attempts

ADS builds and manages the entire decision stack - from designing and deploying the ML models that assess the riskiness of a transaction, to building the tools the Risk team uses to manage and fight fraud. We seek to standardize and automate repetitive work so we can spend more time on experiments and high-leverage projects

We value collaboration and team ownership. Data scientists in Signifyd are true “full stack” operators, requiring knowledge of how transaction information received via our API traverses its way through our system and into the models we are responsible for building. When you test a hypothesis at Signifyd, you’re responsible for the end-to-end development, deployment, and evaluation process. This is a massive responsibility, and no one should feel like they're solving a hard problem alone. Together we help each other develop our skillsets through peer review of experiments and code, group paper study to deepen our machine learning and statistical understanding, and frequent knowledge-sharing through live demos, write-ups, and cross-team projects. All team members are expected and encouraged to weigh in as an external reviewer on a peer's idea or approach, regardless of level

A couple quick notes on the Signifyd culture