As a key member of the Risk and Identity Solutions modeling organization (VPM), you will focus on developing and implementing best practices for deploying machine learning models within large-scale data science projects. Your role will involve bridging the gap between data engineering and data science, ensuring the seamless deployment and automation of ML/AI solutions.
Key responsibilities
- Conducting exploratory data analysis (EDA) using Python's scientific libraries, including NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.
- Implementing and managing model development frameworks such as MLflow for lifecycle management.
- Utilizing Papermill to parameterize and execute Jupyter Notebooks efficiently.
- Developing and optimizing MLOps pipelines, incorporating continuous integration and deployment (CI/CD) strategies for machine learning models.
- Building and maintaining robust data pipelines and feature engineering processes to support ML workflows.
- Engineering, testing, validating, and deploying ML models for high-performance use cases.
- Leveraging AWS SageMaker for end-to-end model building, training, and deployment.
- Implementing best practices for deploying machine learning models at scale in production environments.
- Collaborating closely with global stakeholders, including product managers, data scientists, and researchers, to define and execute a strategic roadmap.
- Managing and maintaining DevOps tools to enhance automation and production readiness.
- Working with containerized and virtualized environments such as Docker and Kubernetes for scalable deployment.
- Enhancing data engineering capabilities by working with big data technologies, including Hadoop, Spark, and NoSQL databases.
Qualifications & Skills
Basic Qualifications
- Bachelor's degree with at least 4 years of experience or a Master's degree with at least 2 years of experience in computer science, statistics, finance, economics, or a related analytical field.
- Strong programming skills in Python (or R), with expertise in data analysis and machine learning frameworks.
- Hands-on experience in working with structured, unstructured, and streaming datasets for machine learning applications.
- Proficiency in Unix/Shell scripting and scheduling tools like Oozie and Airflow.
- Experience with SQL for querying and processing large datasets using platforms such as Hadoop, EMR, and NoSQL databases.
- Familiarity with Papermill for automating Jupyter Notebook execution.
Preferred Qualifications
- Knowledge of model-serving engines such as TensorFlow Serving, Triton Inference Server, etc.
- Experience building and maintaining Spark pipelines to create feature stores for ML models.
- Strong understanding of ETL processes for data quality, reporting, and predictive modeling.
- Hands-on expertise with AWS SageMaker for scalable ML model development and deployment.
- Familiarity with big data and real-time streaming technologies, including Kafka, Redis, Flink, and related tools.
- Experience implementing and maintaining CI/CD pipelines for machine learning models using DevOps best practices.
Why Join Visa?
At Visa, we believe in creating an environment where our employees can thrive and make a real impact. We offer competitive compensation, comprehensive benefits, and opportunities for continuous learning and career growth.
As a hybrid role based in Bangalore, India, you will have the flexibility to work in-office as required while collaborating with global teams in a dynamic and innovative environment.
If you are passionate about data engineering, MLOps, and driving innovation in risk management solutions, we encourage you to apply and be part of our mission to transform the future of digital payments.
Equal Opportunity Employer Statement
Visa is an equal opportunity employer. All 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 also considers qualified applicants with criminal histories in accordance with applicable laws and regulations.