Data Scientist Lead [Multiple Positions Available]

JPMorganChase • Jacksonville, FL

8d • Onsite

About The Position

Lead advanced analysis of petabyte-scale, multi-source datasets-including streaming, cloud, and legacy systems-to support reporting, predictive modeling, and Al-driven solutions. Translate business objectives into scalable technical strategies and deliver end-to-end data solutions that maximize product value. Design and implement AI-enabled data transformation pipelines using NLP and generative AI to automate metadata tagging, enhance data quality, and streamline documentation. Oversee multiple data initiatives by managing priorities, milestones, KPIs, and stakeholder communication while mitigating risks and inefficiencies. Partner cross-functionally with Product, Marketing, Operations, Technology, and Data teams to strengthen the data ecosystem and ensure foundational data needs are met. Apply domain expertise in Home Lending analytics, mentor junior team members, and promote best practices in data management and innovation.

Requirements

• Bachelor's degree in Computer Engineering, Computer Science, Management Information Systems, Data & Analytics or related field of study plus 7 years of experience in the job offered or as Data Scientist, Business Intelligence Developer, Software Engineer, or related occupation.
• Developing production-grade data pipelines and ETL workflows using Python including libraries Pandas, NumPy, and SQLAlchemy and PySpark for distributed data processing of datasets.
• Developing, optimizing, and maintaining SQL stored procedures with dynamic parameterization, error handling, and performance tuning for operational reporting systems in Microsoft SQL Server, Oracle database, Teradata and Snowflake.
• Architecting and implementing big data solutions using Hadoop ecosystem components including HDFS, Hive, and MapReduce for processing multi-terabyte datasets.
• Conducting PySpark optimization techniques including partitioning strategies, broadcast joins, and memory management for cluster computing environments.
• Designing and deploying end-to-end data solutions on AWS cloud infrastructure, including S3 for data lake architecture with lifecycle policies and versioning and RDS and Aurora for relational database management with high availability configurations.
• Using Amazon Redshift for data warehousing, optimizing distribution keys and sort keys for performance and scalability.
• Conducting serverless SQL querying and analysis of petabyte-scale datasets ssing Amazon Athena.
• Orchestrating and automating data workflows using AWS services including Glue, Lambda, EMR, and Step Functions.
• Administering and optimizing Snowflake for enterprise data warehousing, including virtual warehousing, time travel, zero-copy cloning, building data pipelines, and implementing streams and tasks for automated and real-time data processing.
• Designing and optimizing Teradata and Oracle databases, performing performance tuning, workload management, data modeling, PL/SQL development, partitioning, indexing, and query optimization to ensure efficient, high-volume data processing.
• Developing and maintaining Microsoft SQL Server, designing ETL workflows with SSIS, implementing indexing strategies, high availability, T-SQL analytics, and ensuring secure, scalable, and high-performance data solutions.
• Designing normalized and denormalized database schemas supporting OLTP and OLAP workloads.
• Developing interactive dashboards and reports using Tableau including calculated fields, parameters, and LOD expressions.
• Developing interactive dashboards and reports using Power BI including DAX, Power Query, and custom visuals.
• Data quality management processes including profiling, cleansing, validation, and monitoring with measurable quality metrics including accuracy, completeness, and consistency using Tableau and Power BI.
• Participating in sprint planning, daily standups, retrospectives, and delivering iterative data solutions in Agile Scrum environments.
• Applying project management techniques including scope definition, resource allocation, risk management, and stakeholder communication for data initiatives.

Responsibilities

• Lead advanced analysis of petabyte-scale, multi-source datasets-including streaming, cloud, and legacy systems-to support reporting, predictive modeling, and Al-driven solutions.
• Translate business objectives into scalable technical strategies and deliver end-to-end data solutions that maximize product value.
• Design and implement AI-enabled data transformation pipelines using NLP and generative AI to automate metadata tagging, enhance data quality, and streamline documentation.
• Oversee multiple data initiatives by managing priorities, milestones, KPIs, and stakeholder communication while mitigating risks and inefficiencies.
• Partner cross-functionally with Product, Marketing, Operations, Technology, and Data teams to strengthen the data ecosystem and ensure foundational data needs are met.
• Apply domain expertise in Home Lending analytics, mentor junior team members, and promote best practices in data management and innovation.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior