We are looking to hire a Data Scientist to lead the AI/ML initiatives in the Cybersecurity Products team. This position will be based out of Warsaw, Poland. In this role, you will:
Be responsible for driving, designing, and building cutting edge innovation in the space of cybersecurity through Artificial Intelligence and Machine Learning - current scope of problems includes behavior biometrics, risk-based authentication, Account Takeover protection, advanced threat detection, smart incident response, AI model threat analysis, and many more.
responsibilities :
Cyber Analytics Product: Research innovation in digital authentication using behavior biometrics capabilities, build applied AI based models and engineer them into the product called Visa Behavior Analytics. You will engage in data science and applied AI related activities for a Visa engineered product to protect against account takeover related threats, continuously enhancing it to combat threats in the secure authentication and perimeter defense space.
Cyber/AI R&D: Research innovation in applying AI to the more general field of cybersecurity, including the protection with and against AI driven technologies, as well as the AI models themselves. You will be using your core competencies around AI and data science and help drive the teams to build models and solutions that work at scale, harnessing Petabytes of data while applying it to products that need to respond with cyber analytics in milliseconds.
Influence & Collaborate: Be able to present results to a cross section of employees, including C-Level and other senior leadership at Visa. You will engage with internal technology, and cyber teams along with global product orgs. In addition, you will collaborate with colleagues in technology and product offices to establish effective, productive business relationships. This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.
Drive the continued innovation and engineering of our existing behavior-based adaptive authentication product and bot/fraud protection product with a high-performance team of data scientists and engineers.
Build innovative solutions and collaborate with engineering and product partners across the global Visa organization that help secure Visa against a variety of threats and attacks.
Provide consultation to more experienced leaders in order to recommend solutions which solve security & other business challenges.
You must have strong technical depth and experience in application of Machine Learning, Deep Learning, and Data Science techniques. On top of that, you should also have a genuine interest in cybersecurity and a desire to build solutions that deliver real impacts to the world. We will rely on your leadership to establish a roadmap and vision as this team engages in existing and new emerging areas.
Support transfer technical knowledge to facilitate implementation of the business solution provided. Document all projects developed, including clear and efficient coding, and write other documentation as needed.
Identify relevant market trends by country, based on a deep analysis of payment industry information. Interacting with several internal and external stakeholders for the strategic definition of analysis and initiatives. Continuously develop and present innovative ideas to improve current business practices within Visa.
requirements-expected :
2+ years of relevant work experience and a Bachelor’s degree, OR 5+ years of relevant work experience.
3 or more years of work experience with a Bachelor’s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD).
3+ years of experience in modern data mining and data science techniques (e.g., regressions, decision trees, ensemble algorithms, neural networks, time series analytics, clustering, anomaly detection, text analytics, etc.).
Candidates with a PhD in a quantitative field, such as Statistics, Mathematics,
Experience in developing and deploying products using Docker, Kubernetes, and the containerization technology stack.
Experience in development of advanced machine learning and deep learning models such as RNN, LSTM, Graph Neural Networks.
Experience and proficiency in working with large language models (LLMs), and familiarity with the associated solution architecture and infrastructure, including Nvidia GPUs.
Experience in leading, building, and supporting scalable and reliable AI/ML- powered systems, enabling rapid prototyping and advanced analytics using modern big data and AI/ML technologies (e.g., Spark, Kafka, TensorFlow, PyTorch) in an agile environment.
Software Engineering background: The candidate must be proficient in Python and at least one object-oriented programming language (Java, Golang, C++, etc.) Golang experience is desired. Candidate must possess software engineering skills and be able to take end-to-end ownerships of analytical models.
Data Wrangling: The candidate must have proficient data wrangling skills with Python, SQL, and other data processing tools/scripts.
Experience with the end-to-end machine learning lifecycle and MLOps, including data preprocessing and feature extraction, model training and evaluation, deployment, and monitoring of AI models in production environments.
Experience working with Docker in both development and deployment workflows, ensuring smooth transitions from development to production environments.
Distributed Systems: practical experience with NoSQL data platforms (e.g., Cassandra, Lakehouse, DynamoDB) and caching technologies like Redis is a plus.
A solid understanding of the Linux networking subsystem, contributing to the stability and performance of deployed AI/ML systems.
A solid understanding of the web applications and APIs, contributing to the front-end accessibility and integration of AI-driven solutions.
benefits :
sharing the costs of sports activities
private medical care
sharing the costs of professional training & courses
