The Staff ML Scientist will collaborate with a team to conduct world-class applied AI research on financial payments data, driving innovation in alignment with Visas strategic vision by incubating new data- and AI-powered products and enhancing existing applications with machine learning and AI. This role represents an exciting opportunity to make key contributions to Visas strategic vision as a world-leading data-driven company. The successful candidate must have strong academic track record and demonstrate excellent statistical, machine learning and software engineering skills. You will be a self-starter comfortable with ambiguity, with strong attention to detail, and excellent collaboration skills.
This is a hybrid position. Expectation of days in office will be confirmed by your Hiring Manager.
responsibilities :
Develop and apply cutting-edge algorithms and models, ranging from classical machine learning to deep learning techniques, including advanced neural network architectures such as Transformers, Graph Neural Networks (GNNs), and other emerging paradigms.
Pioneer and apply novel data science, deep learning, and AI methodologies to address unique business challenges and drive innovation.
Stay up-to-date with the latest research in machine learning, deep learning, and neural network architectures, integrating relevant advancements into business solutions.
Build, experiment with, and implement statistical, machine learning, and deep learning algorithms - including custom techniques as well as industry-standard tools.
Devise and apply advanced methods for explainability and interpretability of deep learning models, including mechanistic interpretability and model transparency techniques.
Develop and implement adaptive learning systems, as well as methods for model validation, A/B testing, and robust performance evaluation.
Collaborate with data engineers, software developers, product teams, and business stakeholders to translate business requirements into impactful machine learning solutions.
Communicate complex technical concepts, findings, and recommendations clearly to both technical and non-technical audiences.
Work with both structured and unstructured data, experimenting with in-house and third-party datasets to evaluate their relevance and value for business objectives.
Automate all stages of the predictive pipeline to streamline development and minimize manual intervention in both development and production environments.
requirements-expected :
5+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience.
5+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience.
MS or PhD in a quantitative discipline such as Statistics, Data Science, Mathematics, Physics, Operations Research, Engineering, or a related field, with demonstrated strength in machine learning, deep learning, or equivalent practical experience.
7+ years of experience applying data science and machine learning to solve business problems, with proficient Python coding skills and deep expertise in statistical analysis.
Exceptional problem-solving abilities, with experience designing and implementing complex data science solutions.
Hands-on experience developing and deploying deep learning models using PyTorch, including model architecture design and optimization.
Strong background in deep learning, including architectures such as Transformers. Experience with Large Language Models (LLMs), natural language processing (NLP), and advanced expertise in time-series modeling techniques.
Proficiency with big data tools and frameworks (e.g., Spark, Hadoop), and practical experience implementing MLOps practices such as model versioning, automated deployment, and production monitoring.
Strong understanding of model interpretability techniques, with the ability to analyze, articulate, and justify the decision-making processes of machine learning and deep learning models.
benefits :
sharing the costs of sports activities
private medical care
sharing the costs of professional training & courses
