The Risk and Identity Solutions (RaIS) team provides risk management services for banks, merchants, and other payment networks. Machine learning and AI models are the heart of the real-time insights used by our clients to manage risk. Created by the Visa Predictive Models (VPM) team, continual improvement and efficient deployment of these models is essential for our future success. To support our rapidly growing suite of predictive models we are looking for engineers who are passionate about managing large volumes of data, creating efficient, automated processes and standardizing ML/AI tools.
This is a great opportunity to work with a new Engineering and MLOps team to scale and structure large scale data engineering and ML/AI that drives significant revenue for Visa. As a member of the Risk and Identify Solutions modeling organization (VPM), your role will involve developing and implementing practices that will allow deployment of machine learning models in large data science projects. You must be a hands-on expert able to navigate both data engineering and data science disciplines to build effective engineering solutions that support ML/AI models. You will partner closely with global stakeholders in RaIS Product, VPM Data Science and Visa Research to help create and prioritize our strategic roadmap. You will then leverage your expert technical knowledge of data engineering, tools and data architecture in the design and creation of the solutions on our roadmap. The position is based at Visas offices in Bangalore, India.
Essential functions
Team members working for this role deploy, manage, and optimize data pipelines and machine learning models in production environments, ensuring smooth integration and efficient operations. The team also takes a data scientist s model and make it accessible to the software that utilizes it. The essential functions of this role include: ETL processes: The role also involves developing and executing large scale ETL processes to support data quality, reporting, data marts, and predictive modeling. Spark pipelines: The role requires building and maintaining efficient and robust Spark pipelines to create and access data sets and feature stores for ML models. Distributed computing: This role involves developing distributed applications. Performance optimization: This role involves a lot of performance optimization on the existing data pipelines developed in Spark or other distributed frameworks. Infrastructure Management: This role involves managing VPM ML platform infra, datasets, data governance, application asset management, Migrations. Data pipelines orchestration: Working with tools like Apache air flow, Control M to deploy and manage data workflows. Might develop custom tools for effective data pipeline orchestrations. Collaboration with Technology teams: The role involves working with Data Science teams, Visa Research, and other Technology teams to leverage and provide feedback on ML systems and tools. Technical documentation and innovation: The role requires defining and building technical and data documentation, using code version control systems, ensuring data accuracy and consistency, and suggesting new ideas for innovation. This is a hybrid position. Expectation of days in office will be confirmed by your Hiring Manager.
Basic Qualifications:
- 2+ years of relevant work experience and a Bachelors degree, OR 5+ years of relevant work experience
- Working knowledge of Hadoop ecosystem and associated technologies (e.g. HDFS, MapReduce, YARN, Spark, Kafka, MLlib, GraphX, iPython, sci-kit, Pandas etc.)
Preferred Qualifications:
- Experience working in Linux/Unix environment and exposure to command line utilities.
- Strong Development experience in one or more than one of the following: Golang, Java, Python, Rust, and C/C++.
- Hands-on experience working with large scale data ingestion, processing, and storage in the Hadoop ecosystem.
- Experience with complex, high volume, multi-dimensional data, as well as machine learning models based on unstructured, structured, and streaming datasets.
- Experience in writing and optimizing SQL queries in Big data environment.
- Experience creating/supporting production software/systems and have expertise on resolving performance bottlenecks for production systems
- Experience working with scheduling tools (Airflow, Control-M) and building data processing orchestration workflows.
- Strong written, verbal, and interpersonal skills needed to effectively communicate technical insights and recommendations with business customers and leadership team.
- Experience working with technology and business teams on Data Governance, Data Quality and Data Architecture initiatives.
