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Visa

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3.5

based on 361 Reviews

Video summary

142 Visa Jobs

Senior ML Scientist (EDA and building and training ML models)

Visa Inc

3.5

based on 361 Reviews

4-9 years

Bangalore / Bengaluru

1 vacancy

Senior ML Scientist (EDA and building and training ML models)

Visa

posted 1hr ago

Job Role Insights

Flexible timing

Key skills for the job

Data Analysis Python SQL Machine Learning Analytical Chemistry Risk Management

+ 2 more

Job Description

This is a great opportunity to work with a new Data 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.

Essential Functions:

Proficient in exploratory data analysis (EDA) using Pythons scientific libraries including numpy, pandas, matplotlib, seaborn, and scikit-learn
Exposure to model development frameworks like MLFlow
Experience using Papermill for parameterizing and executing Jupyter Notebooks
Strong development experience in at least one of the following: Python, R (preferably Python)
Implementation of MLOps practices including continuous integration and deployment (CI/CD) for ML models
Hands on experience in building and maintaining data pipelines, feature engineering pipelines and comfortable with core ML concepts.
Hands-on experience in engineering, testing, validating, and productizing ML models for high-performance use cases
Hands-on experience with AWS Sagemaker for building, training, and deploying ML models
Develop and implement practices for deploying machine learning models in large data science projects
Proven experience in building and training complex ML models
Experience using and maintaining DevOps tools and implementing automations for production
Additional knowledge of AWS services and ecosystems
Experience working with containerized and virtualized environments (Docker, K8s)

Basic Qualification

4+ yrs. work experience with a Bachelor s Degree or 2+ years of work experience with a Masters or Advanced Degree in an analytical field such as computer science, statistics, finance, economics or relevant area.

Technical skills:

Proficient in Exploratory data analysis (EDA) using Pythons scientific libraries including numpy, pandas, matplotlib, seaborn, and scikit-learn
Exposure to frameworks like ML flow for model lifecycle management
Proven experience in building and training complex ML models.
Strong development experience in programming languages, preferably Python
Experience with complex, high-volume, multi-dimensional data, as well as machine learning models based on unstructured, structured, and streaming datasets.
Experience with Unix/Shell or Python scripting and exposure to scheduling tools like Oozie and Airflow.
Experience with SQL for extracting, aggregating, and processing big data pipelines using Hadoop, EMR, and NoSQL Databases.
Experience using Papermill for parameterizing and executing Jupyter Notebooks .

Preferred Qualification

Exposure to model serving engines such as Tensorflow, Triton etc.
Spark Pipelines: Build and maintain efficient and robust Spark pipelines to create and access data sets and feature stores for ML models.
ETL processes: The role also involves developing and executing large scale
ETL processes to support data quality, reporting, data marts, and predictive modeling.
Hands-on experience with AWS SageMaker for building, training, and deploying ML models.
Knowledge of standard Big data and Real Time stack such as Hadoop, Spark, Kafka, Redis, Flink and similar technologies
Implementation of MLOps practices, including continuous integration and deployment (CI/CD) for ML models.

Employment Type: Full Time, Permanent

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