Sift

Senior Data Scientist

Poland

$74,000 – $95,000Compensation
Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
Financial Technology, Fraud Prevention, AI & Machine LearningIndustries

Requirements

Candidates should possess a degree in Statistics, Machine Learning, Computer Science, Applied Mathematics, Operations Research, or a related field, with at least six years of experience working with large datasets using tools such as Jupyter, Pandas, PySpark, PyTorch, or TensorFlow, and at least four years of experience applying AI/ML methodologies in a production environment. Strong proficiency in Python and experience building analytical tools are also required.

Responsibilities

The Senior Data Scientist will research and analyze gaps in machine learning models, summarize emerging fraudulent behavior patterns, define success metrics, propose targeted improvements to enhance model performance, collaborate with engineers to build scalable ML models, develop systems that explain model predictions, communicate effectively to influence stakeholders, leverage anomaly detection algorithms, and ultimately enable low-latency, large-scale fraud prevention for customers.

Skills

Python
Jupyter
Pandas
PySpark
PyTorch
TensorFlow
Machine Learning
Statistical Libraries
Data Analysis
Model Development
Scalable Prediction Systems

Sift

Real-time fraud detection and prevention platform

About Sift

Sift provides a platform focused on detecting and preventing online fraud in real-time, catering to clients in e-commerce, fintech, and digital marketplaces. The platform uses machine learning and artificial intelligence to analyze large datasets, allowing it to identify fraudulent activities effectively. One of its standout features is dynamic friction, which ensures that genuine users have a smooth experience while preventing fraudsters from accessing services. Sift's business model is subscription-based, with fees that depend on transaction volume and service level. Additionally, Sift offers services like chargeback management and dispute resolution, which add further value to its offerings. The company's goal is to enhance digital trust and safety for businesses by providing tools that help them make informed decisions and protect against fraud.

Key Metrics

Bristol, United KingdomHeadquarters
2011Year Founded
$4.4MTotal Funding
SERIES_ACompany Stage
AI & Machine Learning, Financial ServicesIndustries
51-200Employees

Risks

Rise of app-enabled friendly fraud challenges Sift's mobile fraud detection capabilities.
Reliance on third-party delivery apps by QSRs introduces new fraud risks for Sift.
Complex payment processes may complicate Sift's integration and effectiveness in fraud prevention.

Differentiation

Sift offers a comprehensive platform for real-time online fraud detection and prevention.
The company uses machine learning and AI to analyze vast amounts of data effectively.
Sift's dynamic friction feature ensures seamless user experience while blocking fraudsters.

Upsides

Growing demand for AI-driven fraud detection in QSRs presents expansion opportunities for Sift.
Digital-first banks' need for effective authentication aligns with Sift's fraud prevention solutions.
Global trend towards secure payment systems supports Sift's mission for digital trust and safety.

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