Data Scientist, Face Biometrics

• Developing machine learning algorithms for face authentication solutions
• Researching, prototyping, and evaluating presentation attack detection and fraud prevention methods
• Testing and configuring face algorithms to meet performance and accuracy standards
• Running rigorous experiments, interpreting findings, and translating outcomes into deployable models and policies

Requirements

• Hands on experience in at least two of the areas listed below:
• Computer Vision & Deep Learning
• Proficiency with deep learning frameworks such as PyTorch or TensorFlow
• Strong programming skills in Python and/or C++
• Data & Experimental Methods
• Experience handling, preprocessing, and analysing large-scale datasets
• Image Recognition & Matching
• Liveness & Anti-Spoofing
• Bachelors, Masters, or PhD in Computer Science, Electrical Engineering, Mathematics, or a related field is preferred.

Core Competencies

Demonstrates expertise in developing machine learning algorithms for face authentication, with a strong focus on computer vision, deep learning, and data analysis. Proficient in utilizing frameworks like PyTorch and TensorFlow to create and evaluate robust models for liveness detection and anti-spoofing.

Highest-signal resume keywords

• Machine Learning Algorithms
• Computer Vision
• Deep Learning Frameworks
• Python Programming
• Data Analysis

ATS Optimization Keywords Hard Skills

• Machine Learning
• Computer Vision
• Deep Learning
• Python
• C++
• Data Preprocessing
• Image Recognition
• Liveness Detection
• Anti-Spoofing
• Experimental Methods

Industry Keywords

• Face Authentication
• Fraud Prevention
• Presentation Attack Detection
• Large-Scale Datasets
• Performance Standards

Tools & Technologies

• PyTorch
• TensorFlow