Data Scientist II

Menlo Ventures; Ciudad de México; A distancia; USD 25,000 - 45,000; Jornada completa

Descripción de la vacante

A leading technology company is seeking a Data Scientist to enhance their fraud detection capabilities. The ideal candidate will have a strong background in statistical analysis, programming in Python and Java, and experience building machine learning models. Join a collaborative and dynamic team dedicated to improving eCommerce experiences while tackling fraud in real-time.

Formación

• 3+ years of hands-on statistical analysis experience.
• Proficient in designing experiments and collecting data.
• Fluent in English.

Responsabilidades

• Building production machine learning models to identify fraud.
• Designing new algorithms optimizing the fraud protection platform.
• Collaborating with engineering teams to enhance machine learning pipeline.

Conocimientos

Statistical analysis

Data analysis

Communication

Educación

Bachelor's degree in computer science, applied mathematics, economics, or an analytical field

Advanced degree (M.S. or Ph.D) in an analytical field

Herramientas

Python

Java

SQL

Linux command line

Descripción del empleo

The Data Science team at Signifyd builds the models that power our fraud detection engine. Our machine learning pipeline keeps us one step ahead of fraudsters and their constantly evolving tactics and our research and experiments develop into new products that improve the merchant payments experience.

We expect our data scientists to be hands-on. We carry solutions from a brainstorm to experimentation and all the way to deployment. We’re a varied group with a diversity of strengths -- some team members came to us from academic backgrounds, others from engineering, some from big companies and some from small, but all of us are curious and collaborative.

We are looking for someone who embodies our company values:

Curious and Hungry: Be willing to do research and design experiments by being hands-on

Tenacious: Creating something new is hard work, and our Data Scientist team never gives up

Customer Passion: Be the backbone to our platform, and help us stay ahead of fraudsters

Design for Scale: Work with the rest of the Data Science team to make fraud protection at scale possible

Agile: Some days you may spend doing research and designing experiments while others are spent using your analytical toolbox to surface insights into real-time fraud attacks.

Roll Up Your Sleeves: Partner closely internally to learn from others, and succeed as a team

How you’ll have an impact:

• Building production machine learning models that identify fraud
• Designing new algorithms that optimize all the key components of the Signifyd Commerce Protection Platform
• Writing production and offline analytical code in Python and Java
• Researching real-time emerging fraud patterns with the Risk Analysis team
• Working with distributed data pipelines
• Communicating complex ideas effectively to a variety of audiences
• Collaborating with engineering teams to continuously strengthen our machine learning pipeline
• Mentoring other members of the team

Past experience you’ll need:

• Bachelor's degree in computer science, applied mathematics, economics, or an analytical field
• An advanced degree (M.S. or Ph.D) in an analytical field is a plus
• At least 3+ years of experience
• Hands-on statistical analysis with a solid fundamental understanding
• Designing experiments and collecting data
• Writing code and reviewing others’ in a shared codebase, preferably in Python and Java
• Practical SQL knowledge
• Familiarity with the Linux command line
• Fluent in English

Experience we love to see:

• Data analysis in a distributed environment
• Passion for writing well-tested production-grade code
• Using visualizations to communicate analytical results to stakeholders outside your team
• Working directly with Go-to-Market teams
• Previous work in fraud, payments, or e-commerce

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

Check out our Director of Data Science featured in Built In

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