Our Digital Data Science Department is focused on building predictive solutions to support mobile app and online banking operations
Requirements
Knowledge of statistics and data science use cases
Programming abilities in SQL, Python, R, or similar languages in data exploration, data preparation, modeling, prediction, and statistical analysis
Ability to learn and use data and cloud environments such as Azure and Databricks
Strong knowledge of statistics and machine learning techniques and tools including logistic regression, XGBoost, neural networks, NLP, and clustering
Familiarity with task-tracking tools (ADO, ServiceNow, Asana, Jira, etc.)
Pursuing a degree in data science, statistics, mathematics, or degrees in similar quantitative fields
Responsibilities
Perform research and analysis on emerging trends in mobile banking data science domains; may include quantitative and/or qualitative analysis
Explore datasets and existing available technologies and tools
Write code in Python and SQL to summarize and analyze data
Analyze and interpret results with some complexity
Combine knowledge of data science techniques with industry research on solutions in the mobile banking sector, including but not limited to logistic regression, random forest, XGBoost, neural networks, NLP, k-means clustering, ARIMA, and prophet forecasting
Develop whitepapers and research summaries to help strategically target new model development initiatives
Collaborate with team members and participate in team project planning
Other
Must be pursuing a degree from an accredited college/university
Effective organizational, planning and time management skills
Ability to work independently and in a team environment
Communication and data storytelling presentation skills of technical material
