Senior Machine Learning Engineer - Machine Learning Infrastructure
FlipSalary not specified
Full Time
Senior (5 to 8 years)
Not SpecifiedCompensation
Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level
Full TimeJob Type
UnknownVisa
Fintech, Payments, Financial ServicesIndustries
Requirements
- 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 directly related experience applying data science and machine learning to solve business problems
- 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
- Strong academic track record
- Excellent statistical, machine learning, and software engineering skills
- Self-starter comfortable with ambiguity, strong attention to detail, and excellent collaboration skills
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
Skills
Key technologies and capabilities for this role
Questions & Answers
Common questions about this position
Is this role remote or on-site?
This is an on-site position.
What key skills are required for the Staff Data Scientist role?
The role requires strong statistical, machine learning, and software engineering skills, along with expertise in developing classical machine learning, deep learning techniques including Transformers and Graph Neural Networks, and skills in model explainability, adaptive learning systems, and collaboration.
What is the salary or compensation for this position?
This information is not specified in the job description.
What is the company culture like at Visa?
Visa emphasizes a purpose-driven culture focused on uplifting everyone through innovative payments technology, collaboration across teams including data engineers, software developers, product teams, and business stakeholders, in a world-leading data-driven environment.
What makes a strong candidate for this Staff Data Scientist position?
A strong candidate will have a strong academic track record, be a self-starter comfortable with ambiguity, possess strong attention to detail, excellent collaboration skills, and the ability to stay up-to-date with the latest ML/AI research while pioneering novel methodologies.
Visa
Global digital payment network provider
About Visa
Visa operates a global digital payment network that facilitates electronic payments for millions of people daily. The company connects consumers, businesses, financial institutions, and governments, allowing them to make transactions using Visa cards. Each time a card is used, Visa earns money through transaction, service, and data processing fees. Unlike many competitors, Visa focuses on expanding access to financial services for underserved communities and supporting local economies. The company's goal is to promote financial inclusivity and drive sustainable commerce, ensuring that more people can participate in the global economy.
San Francisco, CaliforniaHeadquarters
1958Year Founded
$55.8MTotal Funding
ANGEL_INDIVIDUALCompany Stage
Fintech, Financial ServicesIndustries
10,001+Employees
Benefits
Health Insurance.
Life Insurance.
Dental Insurance.
Disability Insurance.
Accidental Death & Dismemberment Insurance.
Risks
CBDCs could reduce reliance on Visa's payment network.
'Buy Now, Pay Later' services may decrease traditional credit card transactions.
Fintech startups offering zero-fee transactions could pressure Visa's revenue model.
Differentiation
Visa operates a global digital payment network connecting millions daily.
The company focuses on financial inclusivity and sustainability in its operations.
Visa collaborates with central banks on Central Bank Digital Currencies (CBDCs).
Upsides
Visa's partnership with fintechs enhances cross-border payment solutions.
Adoption of blockchain technology could revolutionize digital payments.
AI-driven fraud detection systems are reducing fraudulent activities.
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