Associate Data Scientist (End to End Pipeline development of Machine Learning & ML Ops)

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

As our team continues to expand rapidly, we are seeking to hire an Associate Data Scientist. The ideal candidate will possess experience and skills in the following areas:

• Developing and implementing a machine learning solution for our Global Solutions team. This includes managing the entire lifecycle of machine learning models, from their creation and testing to their deployment and monitoring on a large (global) scale.
• Utilizing Visa’s extensive data assets to create innovative, data-driven analytical solutions. These solutions should combine business knowledge, statistical rigor, and problem-solving skills to address significant business challenges.
• Furthermore, you will be responsible for extensive collaboration with regional stakeholders and counterparts, in order to build scalable practice globally.
• Work as individual contributor who can develop/retrain exiting ML Solutions and build automated MLOps pipeline to scale regional models for Global Clients developing scalable Inferencing and Monitoring pipeline.
• Developing automated pipelines of MLOps component like model Inferencing, model Validation for multiple models, quality check and automated retraining if the model fails and selecting best models through a control/target framework.
• Experience in Marketing Models, Customer Churn Model, Multiclass Classification Model, Models with Imbalances Dataset
• Strengths in Machine Learning, Model Monitoring/Validation, Basic Statistical Concepts, OOPs and Functional Programming
• Experience with Clustering, Classification and Regression ML Algorithms likes Random Forest, XGBoost, K-Means, Logistic Regression etc.
• Understanding of metrics and statistical concept to develop and automate model monitoring and quality check framework

This position is located in Visa’s offices in Bengaluru, India. It offers a fantastic opportunity for those looking to make a significant impact in the field of Data Science and Machine Learning.

Essential Functions

• Work in a team that generates business insights based on big data, identify actionable recommendations, and communicate findings
• Use statistical concepts, predictive modeling and designing analytical framework to optimize customer experience, revenue generation, and other business outcomes.
• Work as a team in development, implementation, maintenance, and redevelopment of machine learning models, and implement ML Ops best practices. Ensure models are built with scalability, flexibility, and production deployment in mind.
• Create necessary validation and documentation to support the model approval process with the Model Risk Management group to make it production ready.
• Collaborate with Data engineers, Data scientists and various groups within the organization to identify areas of improvement, bottlenecks and build automation and re-usable frameworks/processes.

Strategic and Functional Excellence

• Ability to translate data and technical concepts into requirements documents, business cases and user stories.
• Results-oriented with strong problem-solving skills and demonstrated intellectual and analytical rigor
• Good business acumen with a track record in solving business problems through data-driven quantitative methodologies.
• Very detailed oriented, is expected to ensure highest level of quality/rigor in reports and data analysis
• Should have strong problem-solving capabilities and ability to quickly propose feasible solutions and effectively communicate strategy and risk mitigation approaches to leadership
• Demonstrated ability to incorporate new techniques to solve business problems

 Stakeholder Management

• Strong interpersonal skills to build credibility with team members and leaders across the function as well as the organization
• Team-oriented, collaborative, and flexible, with proven ability to build strong working relationships with internal and external partners

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.

Qualifications

Basic Qualifications
Bachelor’s degree, OR 3 years of relevant work experience

Preferred Qualifications
Associate: 2 or more years of work experience
•1-2 years of experience of development of robust and scalable AI/ML/Data Science solutions and products for large scale B2C applications
•Bachelor’s degree in Statistics, Operations Research, Applied Mathematics, Economics, Data Science, Business Analytics, Computer Science, Marketing Research or a related technical field
•Hands-on experience with modern distributed systems such as Hadoop/SQL or Apache Spark
•Hands-on experience with Python and other data analytics/programming tools such as Python
•Hands-on experience in the application of predictive modeling, machine learning techniques, ML-Ops

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.