Visa Consulting & Analytics (VCA) is Visas consulting division, serving Visas clients (including card issuers, acquirers, sellers and fintechs) and solving their strategic problems focused on improving performance and profitability. Drawing on our expertise in strategy consulting, payments, data science, marketing, operational and macroeconomics, VCA drives high impact and tangible financial results. As VCA is scaling up, it is becoming imperative to scale existing data driven solutions and the Global Data Science team based in Bangalore (India), is responsible for globalizing data driven solutions and models, and making sure we use cutting edge technologies to deliver our offerings seamlessly to clients across the globe. The team uses our extraordinarily rich data set that spans more than 3.5 billioncards globally and captures more than 100 billion transactions in a single year. Our focus lies on building creative solutions that have an immediate impact on the business of our highly analytical partners. We work in complementary teams comprising members from Data Science and various groups at Visa. To support our rapidly growing group we are looking for Data and ML engineers who are equally passionate about the opportunity to use Visas rich data to tackle meaningful business problems.
As an ML/Data Engineer, you will be part of the VCAs Global Data Engineering and Technology function in building and managing the pipelines of global AI/ML models, MLOps Pipeline and engineering solutions. The ML/Data Engineer takes responsibility to build the required blocks for seamless processing of the Data science models including frameworks for feature selection, data preparation, model re-training, model performance and scoring optimization. The position is based at Visas offices in Bangalore, India.
Work closely with Data Scientists to determine and refine machine learning objectives. Ensure that algorithms generate recommendations as expected by testing and training the ML models by developing approaches/functions to analyze huge volumes of historical data. Build MLOps pipelines to support development, experimentation, continuous integration, continuous delivery, verification/ validation, and monitoring of AI/ML models Run tests, perform statistical analysis, and interpret test results executing the ML models. Model re-training, performance evaluation and score optimization for existing ML models. Create necessary validation and documentation to support the Model approval process with the Model Risk Management group to make it production ready. Automate the end-to-end deployment and training steps as part of productionizing the ML models. Create required documentation for the Ops team to take the ML models to production. Solve complex problems with big data datasets as well as optimize existing machine learning libraries and frameworks. Provide quality data solutions in a timely manner and be responsible for data governance and integrity while meeting objectives and maintaining SLAs. Proficient in operationalizing some or all of the following techniques: Linear & Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Markov Chain, Monte Carlo, Gibbs Sampling, Support Vector Machines, Deep Learning techniques
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 two days a week, Tuesdays and Wednesdays with a general guidepost of being in the office 50 PERCENTof the time based on business needs.
Professional Experience: Minimum of 4years of analytics expertise in building Data and ML pipelines to the models built by the Data Scientist community 4yrs. work experience with a Bachelors Degree or 3years of work experience with a Masters or Advanced Degree with specialization in Computer science, Information science, Statistics, Data Engineering and Analytics or relevant area. Good understanding of the Payments and Banking Industry including aspects such as consumer credit, consumer debit, prepaid, small business, commercial, co-branded and merchant
Technical Expertise: Experience in building robust data pipelines and writing ETL/ELT code (PySpark, Hive) Experience working with scheduling tools (Airflow, Oozie) or building data processing orchestration workflows Hands-on experience working with large scale data ingestion, processing, and storage in the Hadoop ecosystem Experience in writing and optimizing SQL queries in Big data environment. Familiarity with both common computing environments (e.g. Linux, Shell Scripting) and commonly-used IDEs (Jupyter Notebooks) Ability to build ML/data pipelines (e.g. ETL, data preparation, feature selection, data aggregation and analysis) using PySpark. Experience creating/supporting production software/systems and a proven track record of identifying and resolving performance bottlenecks for production systems. Experience working in building and integrating the code in the defined CI/CD framework using git Preferred experience with Visualization Tools like Tableau, Power BI and D3 Exposure to machine learning models based on unstructured, structured, and streaming datasets.
Business Skills: Ability to translate data and technical concepts into requirements documents, business cases and user stories. Good understanding of agile working practices and related program management skills. Good communication and presentation skills with ability to interact with different cross-functional team members at varying levels Ability to learn new tools and paradigms as data science continues to evolve at Visa and elsewhere. Demonstrated intellectual and analytical rigor, team oriented, energetic, collaborative, diplomatic, and flexible style.