Requirements

Expected technologies

Python

Docker

SQL

Optional technologies

Transformers

Our requirements

• Proficient Software Engineering Skills: A strong track record of designing, developing, and deploying ML models in a production environment. Experience with deploying models on Scoring Platform is highly desirable;
• High proficiency in Python;
• Strong Quantitative Foundation: A deep understanding of statistical analysis, probability, and a wide range of machine learning and deep learning models;
• MLOps Experience: Hands-on experience with containerization (Docker), CI/CD pipelines, and model monitoring;
• API & Containers: Experience in API schema design and development and in building containerized applications (e.g., using Docker);
• Excellent communication and stakeholder management skills;
• Strong educational background in Computer Science, Information Technology, or a related field;
• Language proficiency: Advanced English (minimum B2 level).

Optional

• Experience working with Fraud Detection or Credit Underwriting.
• Academic background in Physics, ML, Data Science, Mathematics, or Biology.
• Prior experience as a Senior Data Scientist in a regulated environment or fintech context.

Your responsibilities

• End-to-End Model Ownership: Design, develop, and ship production-grade machine learning models. You will have the autonomy to own your solutions, considering everything from memory footprint to latency;
• Research and Innovation: Dive deep into statistical analysis, probability, and machine learning/deep learning models. You'll be expected to critically evaluate existing solutions and, when necessary, develop novel approaches. A foundational understanding of NLP architectures like Transformers is a plus;
• Engineering: Build and maintain containerized models and develop comprehensive model monitoring solutions to track metrics like variable drift;
• Streamlined Deployment: Create and manage CI/CD pipelines for our machine learning models and contribute to the design and development of APIs;
• Data Expertise: Utilize your SQL proficiency to perform in-depth data analysis and feature engineering.