Senior Manager, Advanced Data Analytics and Insights

Job Description

Generates analytical and data insights to drive business actions using SQL, Qlik/Tableau, Python, and R. Creates complex, automated analytics solutions using Text Mining and Natural Language Processing (NLP) tools. Extracts and analyzes complex information from large structured and unstructured datasets using data mining and data modeling, NLP, and Machine Learning (ML). Creates and manages Elasticsearch indexes and develops queries using KQL (Kibana Query Language) and DSL (Domain Specific Language).

Creates custom visualizations in Kibana and Tableau. Analyzes large volumes of structured and unstructured data in Elasticsearch.

Cleans and manipulates large volumes of raw data for analysis by statistical software.

Improves data analytics and insights capabilities using Adobe analytics.

Formulates and applies mathematical modeling and other optimizing methods to develop and interpret information that assists with decision making.

Delivers presentations of mathematical modeling and data analysis to management and other end users.

Oversees daily, weekly, and monthly reporting cycles across analytic environments.

Analyzes large data sets to develop new processes, perform calculations, identify anomalies, and/or product new reporting capabilities.

Mentors junior team members.

Works in a fast-paced environment delivering tactical and strategic technology solutions.

Establishes project management data analytics and insights projects.

Contributes to the estimation, planning, analysis, design, and development of projects.

Fosters innovative technology solutions to resolve complex business problems.

Bachelor’s degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Information Systems Management, Mathematics, Physics, or a closely related field and five (5) years of experience as a Senior Manager, Advanced Data Analytics and Insights (or closely related occupation) performing data analysis and writing code using SQL and Python programming languages.

Or, alternatively, Master’s degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Information Systems Management, Mathematics, Physics, or a closely related field and three (3) years of experience as a Senior Manager, Advanced Data Analytics and Insights (or closely related occupation) performing data analysis and writing code using SQL and Python programming languages.

Candidate must also possess:

Demonstrated Expertise (“DE”) generating insights for customer service strategy and customer experience touchpoints by extracting, transforming, and analyzing unstructured and semi-structured data – text content (call/chat transcripts, social post) and customer-entered feedback (surveys and online tool interactions) – using Natural Language Processing (NLP), LLM models/algorithms to identify sentiment, extract entities, text summarization, and perform text classification; and integrating data from various sources and internal data warehouses by creating standards and repeatable workflows – Elasticsearch, Snowflake, AWS and Oracle – to process at scale.

DE quantifying and monitoring customer activities across channels by developing customer journeys using data preparation and visualization tools – Power BI, Tableau, Kibana and Excel; and furnishing actionable insights that enable business decisions through 360-degree customer data mining using Cloud technologies – Elasticsearch, Snowflake, Oracle, and Amazon Web Services (AWS).

DE identifying key trends and customer behaviors that influence business decisions by building hypotheses for mining digital or live channel data in AWS and Snowflake EDL, using SQL, Python, Elasticsearch and Amazon Web Services (AWS) technologies – Athena, Glue, Sage maker, Kibana, S3, Quicksight, and API Gateway.

DE parsing, summarizing, and visualizing customer journeys across digital or live activities using Python, Tableau, Postman, and API methodologies; tying engagements to financial or operational transactions, using SQL and Python packages; supporting and informing client service strategies by performing statistical analysis, and data extraction, transformation, and summarization within the financial services industry – using statistical methods (inferential statistics, hypothesis testing, and A/B testing) and data analytics software (Python, SQL, Elasticsearch).