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Data Scientist Manager
Data Scientist Manager
The Data Scientist Manager at Visa will be responsible for conducting transaction data analysis, building predictive models using advanced machine learning techniques, and creating user-friendly dashboards. This role involves executing analytic plans, performing QA on deliverables, and exploring new methodologies and best practices in data science.
About the role
What a Data Scientist Manager does at Visa:
- Conducting transaction data analysis with Hadoop/Cloud and big data technologies for internal and external clients and stakeholders, and develop deeper insights into the products using advanced statistical methods
- Creating user-friendly dashboards and presentations
- Building predictive models using advanced machine learning techniques, interpret and present modeling and analytical results to non-technical audience
- Executing on the analytic plan with appropriate data mining and analytic techniques
- Performing QA on data and deliverables by analysts and own deliverables
- Ensuring all project documentation is up to date and all projects are reviewed per analytic plan
- Ensuring project delivery within timelines
- Continually look at the environment to challenge our assumptions around new sources of data, potential analytics partners, tools, talent and infrastructure.
- Explore leading methodologies and best practices to other teams and importing successful methodologies from other international markets
Requirements
- Bachelor's degree in Economics, Finance, Computer Science, Statistics, or related quantitative field
- Minimum of 6 to 8 years of experience in performing data exploration and feature engineering
- Experience in working on multiple projects simultaneously
- Proficiency with modelling software, experience with Python, Hadoop, Hive, Impala or similar instruments
- Practical experience in building and applying machine learning models (regression, clustering, classification: gradient boosting, random forests, linear models, deep learning etc.), understanding in how do these algorithms work and end-to-end development skills from business understanding and data preparation to quality assurance of ML models