Lead Data Scientist, Fingerprint

• Experience:7+ years in Machine Learning, Data Science, and backend development
• Machine Learning and Data Science Expertise:
• Advanced foundations in ML and statistical methodologies
• Strong experience in supervised learning, including gradient boosting and handling high-cardinality categorical data
• Practical experience with semi-supervised and unsupervised learning techniques
• Proficiency in exploratory data analysis and creative problem-solving for dataset collection and performance estimation in the absence of labeled data
• Strong expertise in real-time ML service development, including challenges like real-time inference and model-to-service integration
• Excellent coding skills with expertise in SQL and general software engineering tools (Git, CI/CD pipelines, IDEs, shell scripting)
• Ability to create MVP real-time web services from ML models
• Fluent English for clear communication in a global, remote team

Desirable

• We are seeking a Lead Data Scientist to join our innovative team. In this role, you will focus on improving the accuracy of the Fingerprint Pro Identification Service by applying advanced data science and machine learning techniques in a high-load real-time service setting
• As a Lead Data Scientist, you will lead technical strategies, mentor seasoned engineers in challenging areas of machine learning, and contribute to fostering an engineering-focused, data-driven culture across the Fingerprint team
• You will own data science projects from concept to deployment, ensuring they seamlessly integrate with our real-time identification platform
• Develop data-driven algorithms for Fingerprint Pro Identification Service, applying ML techniques to process raw, noisy, and unlabeled data to extract insights about browsers and devices
• Lead the design and implementation of supervised, semi-supervised, and unsupervised learning approaches to improve our identification capabilities
• Mentor team members in machine learning, data science, and analytics, enhancing the team’s technical expertise
• Conduct exploratory data analysis to investigate ad-hoc questions and address anomalous data
• Design experiments and solutions for ML-related engineering challenges like real-time model inference and training pipeline automation
• Help build an engineering-focused, data-driven culture across the team by sharing tools and effective approaches to data science