Senior Data Scientist - Fraud Risk Analytics (Remote)

feedzai; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Feedzai is seeking a data scientist within the Customer Success Data Science Team. You will understand and clean client data, preprocess with scripts and Python/Scala, and iteratively develop features and models. You will communicate findings to project managers and collaborate with clients and internal teams to improve risk solutions.

The role emphasizes client-facing engagement, model training, and collaboration across Product, Research, and Risk teams on a global scale.

Qualifications

• MSc or PhD in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics or related field.
• Proficient in Machine Learning and model development.
• Knowledge of Big Data technologies such as Spark, Hadoop and related.

Responsibilities

• Understand data provided by clients and its quality.
• Clean and validate data.
• Preprocess data using shell scripts and Python/Java/Scala.
• Iteratively compute features and tune parameters for model quality.
• Communicate findings to project manager and support data science decisions.
• Collaborate with clients' stakeholders (data scientists, engineers, risk managers).
• Work with Product, Research, and other teams to improve processes and tooling.

Skills

Machine Learning

Big Data

Python

Java/Scala

Bash

Monitoring/Optimization

Communication skills

Fraud/Digital domain

Customer facing

Statistics/Data visualization

Tree-based algorithms

Education

MSc/PhD in CS/EE/Statistics/Applied Math/Physics

Tools

Spark

Hadoop

Job description

Feedzai is seeking a data scientist within the Customer Success Data Science Team. You will understand and clean client data, preprocess with scripts and Python/Scala, and iteratively develop features and models. You will communicate findings to project managers and collaborate with clients and internal teams to improve risk solutions.

The role emphasizes client-facing engagement, model training, and collaboration across Product, Research, and Risk teams on a global scale.