Data Scientist [Multiple Positions Available]

DESCRIPTION:

Duties: Coordinate initiatives which require the development and deployment of advanced analytical solutions, including model development for the most complex business needs.

Responsible for business impact and robustness of solutions delivered, including platforms, procedures, models, and test designs.

Probe for unidentified business needs and explore opportunities for team to develop appropriate, data-based approaches to those needs.

Direct business-impacting team activities and articulate to senior management business plans and results of initiatives.

Follow industry trends and test new tools and techniques to solve new/existing business challenges.

Act as a primary contact with senior managers in several business functional areas.

Manage other data scientists and act as a thought-leader in data science across the organization.

This position requires up to 10% domestic travel to JPMC offices for internal meetings.

QUALIFICATIONS:

Minimum education and experience required: Master's degree in Analytics, Business Analytics, Data Science, Statistics, Mathematics or related field of study plus 5 years (60 months) of experience in the job offered or as Data Scientist, Productivity Analytics, Engagement Manager, Solutions Architect, Technology Analyst, or related occupation.

The employer will alternatively accept a PhD in Analytics, Business Analytics, Data Science, Statistics, Mathematics or related field of study plus 3 years (36 months) of experience in the job offered or as Data Scientist, Productivity Analytics, Engagement Manager, Solutions Architect, Technology Analyst, or related occupation.

Skills Required: This position requires experience with the following: Performing data science and data analytics; Creating and analyzing large data sets (e.g.

50M+ rows) using SQL and Python, including data transformations (including log transformation, clipping methods, and data scaling); Performing exploratory data analysis within large enterprise databases (Terabytes) and extract, clean, transform, and load data; Selecting an artificial intelligence (AI) or machine learning (ML) approach for a given business problem; Applying statistical analysis and probability theory to answer business questions; AI and ML modeling including supervised and unsupervised learning models and advanced analytics (including linear and regression, classification, clustering and tree-based models); Accessing Cloud technologies such as AWS; Consuming data from high-performing data warehouses such as Snowflake; Creating data visualizations and Business Intelligence dashboards using Tableau to communicate data findings to non-technical stakeholders; Automating data pipelines and workflows using Alteryx; Managing structured and unstructured data projects that combine multiple sources of data; Statistics, probability, data modeling, automation of workflows, and Python libraries for machine learning (including Pandas, Scikit-learn, Matplotlib, and PySpark); Us...