Analyst, GenAI Process at Visa

Bogota, Capital District, RAP (Especial) Central, Colombia -

Full Time

Start Date

Immediate

Expiry Date

25 Feb, 26

Salary

0.0

Posted On

27 Nov, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Deep Learning, Python, R, Data Analysis, Statistical Analysis, Algorithm Development, Problem Solving, Critical Thinking, Collaboration, Communication, Data Engineering, Data Architecture, User Recommendations, AI Tools, Financial Services

Industry

IT Services and IT Consulting

Description

Company Description Visa is a world leader in payments and technology, with over 259 billion payments transactions flowing safely between consumers, merchants, financial institutions, and government entities in more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable, and secure payments network, enabling individuals, businesses, and economies to thrive while driven by a common purpose – to uplift everyone, everywhere by being the best way to pay and be paid. Make an impact with a purpose-driven industry leader. Join us today and experience Life at Visa. Job Description We are seeking a technically hands-on AI/ML Analyst to join our Global Corporate Services team. This role is ideal for someone with experience in developing generative AI and machine learning tools, particularly within financial services or real estate sectors. The successful candidate will be a self-starter, detail-oriented, and thrive in a fast-paced, global environment. We value creative and driven individuals who love to own their products, and design and implement solutions that create a seamless experience for users. Responsibilities Understand the Global Corporate Services business initiatives and their importance to the growth of Visa. Design and develop machine learning algorithms and deep learning applications and systems for Visa’s Global Corporate Services function Solve complex problems with multilayered data sets, and optimize existing machine learning libraries and frameworks Collaborate with data scientists, administrators, data analysts, data engineers, and data architects on production systems and applications Identify differences in data distribution that could potentially affect model performance in real-world applications Ensure algorithms generate accurate user recommendations Stay up to date with developments in the machine learning industry This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager. Qualifications Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or 3+ years of hands-on experience in machine learning algorithm development or applied statistics Proficiency in Python or R, with expertise in relevant ML libraries such as scikit-learn, pandas, NumPy, TensorFlow, or PyTorch Deep understanding of machine learning concepts, algorithms, and evaluation metrics (e.g., regression, classification, clustering, anomaly detection) Experience optimizing and tuning algorithms for large-scale, real-world deployment Strong analytical, problem-solving, and critical-thinking skills Excellent communication skills, with the ability to explain technical concepts to non-technical audiences Demonstrated ability to work collaboratively in a diverse, cross-functional environment Additional Information Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.

Responsibilities

The Analyst will design and develop machine learning algorithms and deep learning applications for Visa’s Global Corporate Services function. They will collaborate with various data professionals to solve complex problems and optimize existing machine learning libraries.