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Visa is a world leader in digital payments, facilitating more than 215 billion payments transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories each year
The position will be based in Visa’s Bangalore office. We are looking for an individual with deep expertise in building & operationalization of data science solutions based on business needs, using VISA’s big data sets and in certain cases in conjunction with our clients’ data. The candidate is expected to have hands on experience with associated technologies and ability to collaborate across teams and functions to deliver value to VISA and our clients.
The successful candidate will have opportunities to work on huge varieties of problems across Marketing Analytics, Credit Risk, Open banking/ Open data, etc. S/he must have a highly analytical bent of mind and requisite skills to independently analyze and interpret data insights.
We are looking for a talented, technical, proactive, energetic, and passionate person who embraces challenges and is a proven problem solver.
Principal Responsibilities
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 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.
Minimum of bachelor’s degree or equivalent. Masters is preferred
Qualification in Data Science/Computer Science from reputed institutes.
Total 4+ years of experience of which over 3 years being a data scientist or analytics consultant
Hands-on experience in handling end to end data engineering and data analytics tasks. Should have knowledge in developing machine learning models, scaling data solutions, and delivering end-to-end data science projects. Preferably candidates should have hands on in data engineering, data science work experiences OR candidates should have BI, reporting, analytics work experiences.
Experience with big data technologies, data engineering tools is a must. Experience with complex, high volume, multi-dimensional data, as well as machine learning models based on unstructured, structured, and streaming datasets.
Familiar with data handling techniques including cleaning, wrangling, feature development and extraction, feature selection, etc is required. Familiar with typical machine learning models such as Linear & Logistic Regression, Decision Trees, Random Forests, Markov Chains, Support Vector Machines, Neural Networks, Clustering, etc.
Proficient in big data aggregation using Hive, Spark, SQL, R/Python, and familiar with typical deep learning toolkits and packages
Experience with visualization and reporting tools, such as Power BI, Tableau, MicroStrategy, open-source tool, or similar will be a plus. Experience with data engineering (pipeline creation and automation) tools like Airflow, Hue, etc will also be a plus.
Exhibit intellectual curiosity and strive to continually learn, self-motivated and results oriented individual with the ability to handle numerous projects
Ability to learn new tools and paradigms as data science continues to evolve at Visa and elsewhere.
Appreciation of Payments and Banking domain will be a plus
Experience in Marketing Analytics, Credit Risk or Fraud Risk analytics will be a plus
Good communication and presentation skills with ability to interact with different cross-functional team members at varying level
Understanding of credit bureaus and non-traditional data providers will be a plus
Technical Expertise
Experience in writing and optimizing efficient SQL queries and Python/PySpark for data science
Expertise in dashboard and report development using tools such as Tableau/Power BI/Micro Strategy
Good understanding of Agile way of working, tools such as JIRA and practical experience of working in Agile teams.
Experience in deployment, operationalization of Machine Learning models using MLOps techniques
Working knowledge of Hadoop ecosystem and associated technologies, e.g., Hive, Apache Spark, MLlib, GraphX, sci-kit, and Pandas
Experience with Apache Airflow will be an added advantage
Understanding of best-in-class software engineering practices such as DevOps, CI/CD, and job automation will be a plus
Experience with data APIs, containerized deployments will be an added advantage
Experience of cloud-based data science platforms will be an added advantage
Business and Leadership competencies
Results-oriented with strong problem-solving skills and demonstrated intellectual and analytical rigor
Good business acumen with a track record in solving business problems through data-driven quantitative methodologies. Experience in payment, retail banking, or retail merchant industries is preferred
Team oriented, collaborative, diplomatic, and flexible
Detail oriented to ensure highest level of quality/rigor in reports and data analysis
Proven skills in translating analytics output to actionable recommendations and delivery
Experience in presenting ideas and analysis to stakeholders whilst tailoring data-driven results to various audience levels
Exhibits intellectual curiosity and a desire for continuous learning
Demonstrates integrity, maturity, and a constructive approach to business challenges
Role model for the organization and implementing core Visa Values
Respect for the Individuals at all levels in the workplace
Strive for Excellence and extraordinary results
Use sound insights and judgments to make informed decisions in line with business strategy and needs
Ability to influence senior management within and outside Analytics groups
Ability to successfully persuade/influence internal stakeholders for building best-in-class solutions
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