Senior Data Scientist (hybrid work) @ Spyrosoft

Senior Data Scientist (hybrid work) @ Spyrosoft

Polsko

• 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.
• Expertise: 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. Experience working with consumer or partner Fraud Detection/Credit Underwriting would be a plus.
• Ideally, you have: An academic background in Physics, ML, Data Science, Mathematics, or Biology Prior experience as a Senior Data Scientist, ideally within a regulated environment or fintech context

About the project

Join our team in Warsaw, where we’re collaborating on a cutting-edge fintech venture with a global industry leader. Together with our Partner – Klarna, we’re building an IT hub designed to drive innovation in digital payment solutions. We’re on the lookout for top-tier engineers who thrive in dynamic, forward-thinking environments. Spyrosoft is leading the recruitment process, facilitating a seamless experience for candidates who are ready to shape the future of online shopping and payments.This opportunity is ideal for engineers who value independence, proactiveness, and flexibility. Our engagement begins with a B2B contract through Spyrosoft, transitioning to a direct contract with our Partner.We offer a hybrid work model in Warsaw’s vibrant Wola district. English fluency and eligibility to work in Poland are essential, as is the successful completion of a background check to meet the rigorous standards of the financial domain.

,[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. Youll 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. Requirements Proficient Software Engineering ] Requirements: Machine learning, Deep learning, Python, NLP, CI/CD Pipelines, SQL, Python, MLOps, Docker, API, Stakeholder management Tools: Jira.

Kategorie

artificialIntelligence

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