Job Description

A global financial institution seeks a Data Analytics Lead Analyst for their Tampa, Florida location.

Duties include leveraging programming languages such as Python to develop tools for detecting financial crime and identifying threats based on data insights and patterns. The role aims to reduce unproductive alerts and false positives by utilizing typologies and rules. Responsibilities involve analyzing data to discover synthesized insights and performing extensive statistical and exploratory data analyses to identify transaction trends and behavioral patterns.

The analyst will conduct data mining and analysis to prepare datasets for financial threat identification and use big data technologies like Hadoop and Spark to develop data extraction processes for various analytical initiatives and ad-hoc requests supporting the Anti-Money Laundering (AML) organization. Developing machine learning solutions to effectively identify outliers and handling the grouping of large-scale data to extract insights based on detected behavior patterns for data analytics are also key functions.

The role requires leveraging data visualization to design and develop data dashboards, present findings to management, and document information for stakeholders.

Supporting and driving business-led initiatives to improve data quality, productivity, and effectiveness of the AML program, as well as using query analytics like Hive and SQL to fetch and extract large-scale historic and enterprise data sources for extensive data analyses in support of crime detection initiatives, are essential.

A telecommuting/hybrid work schedule may be permitted within a reasonable commuting distance from the worksite, in accordance with company policies.

Requirements include a Master’s degree, or foreign equivalent, in Computer Science, Data Science, Electronic Engineering, or a related field, along with two years of experience in roles such as Applications Support Analyst, Machine Learning Engineer, or Systems Engineer, involving large-scale data mining and analysis for a global bank. Alternatively, a Bachelor’s degree in the stated fields and five years of progressive, post-baccalaureate experience in the specified areas will be considered. The full term of experience must include proficiency in Python and SQL; data visualization for data dashboard design and development; Hive Query Language; and Hadoop and Spark.

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