Data Scientist [Multiple Positions Available]

JPMorgan Chase & Co.•Plano, TX

8d•Onsite

About The Position

Develop and interpret queries, including subqueries, CTEs, and hierarchical data queries, while identifying clauses that affect business logic and reporting accuracy. Build and analyze workflows to ensure accurate data flow and outputs, resolving discrepancies as needed. Validate and inspect dashboards ensuring alignment with business metrics and reporting requirements. Collaborate with stakeholders to gather reporting requirements, conduct data analysis, and support the analytics lifecycle from design to deployment. Implement machine learning, artificial intelligence models via pre trained endpoints or services to automate data extraction and processing, enhancing operational efficiencies and implementing automation and modernization of legacy solutions such as migration to cloud. Manage metadata, data cataloging, and lineage for governance and compliance. Troubleshoot performance bottlenecks in data environments to ensure pipelines meet SLAs and maintain data quality. Build and manage CI/CD pipelines to automate infrastructure deployment and code releases. Transform data from various structured and unstructured datasets.

Requirements

Master's degree in Data Science, Computer Science, Information Systems, or related field of study plus 1 year of experience in the job offered or as Data Scientist, Machine Learning Engineer, Data Analytics, or related occupation.
Bachelor's degree in Data Science, Computer Science, Information Systems, or related field of study plus 3 years of experience in the job offered or as Data Scientist, Machine Learning Engineer, Data Analytics, or related occupation.
Designing and implementing large-scale ETL solutions in Alteryx including data wrangling, transformation, end-to-end workflow orchestration, and automation.
Streamlining repetitive tasks with Alteryx macros including Batch and Interactive.
Developing and Analyzing SQL scripts in Oracle to extract, transform, and analyze data from relational databases using joins, common table expressions, and subqueries.
Performing data blending, cleansing, and transformation using python and SAS programming languages to build Automated datasets.
Developing automated visualizations and dashboards using Tableau to generate actionable insights.
Assisting in the design, review, and implementation of analytical solutions across the data lifecycle.
Developing finance domain datasets and delivering production ready analytical outputs ensuring to meet regulatory and compliance controls.
Translating business requirements into governed reporting solutions adhering to regulatory controls.
Supporting internal audits and technical reviews for finance, risk, and claims data.
Supporting the development of data pipelines that serve pre trained ML models using Statistical Techniques such as PCA and foundational Machine learning models such as Regression, Clustering, or Text processing.
Building scalable data workflows and machine learning based automation workflows on AWS or Microsoft Azure services Using Cloud-based computing, big data processing, and storage services.
Processing and analyzing datasets using distributed data processing framework Apache Spark that enable advanced analytics and intelligent data solutions.
Interpreting model outputs and integrating them into Analytical workflows using ML libraries such as scikit-learn or TensorFlow.

Responsibilities

Develop and interpret queries, including subqueries, CTEs, and hierarchical data queries, identifying clauses that affect business logic and reporting accuracy.
Build and analyze workflows to ensure accurate data flow and outputs, resolving discrepancies.
Validate and inspect dashboards ensuring alignment with business metrics and reporting requirements.
Collaborate with stakeholders to gather reporting requirements, conduct data analysis, and support the analytics lifecycle.
Implement machine learning and artificial intelligence models via pre-trained endpoints or services to automate data extraction and processing.
Enhance operational efficiencies and implement automation and modernization of legacy solutions, such as migration to cloud.
Manage metadata, data cataloging, and lineage for governance and compliance.
Troubleshoot performance bottlenecks in data environments to ensure pipelines meet SLAs and maintain data quality.
Build and manage CI/CD pipelines to automate infrastructure deployment and code releases.
Transform data from various structured and unstructured datasets.