Data Scientist [multiple Positions Available]

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Consumer & Community Banking

Data Scientist at JPMorgan Chase responsible for developing and deploying advanced analytical solutions, including model development for complex business needs. The role involves data analysis, AI/ML modeling, and communicating findings to stakeholders, with a focus on business impact and robustness.

What you'd actually do

  1. Coordinate initiatives which require the development and deployment of advanced analytical solutions, including model development for the most complex business needs.
  2. Responsible for business impact and robustness of solutions delivered, including platforms, procedures, models, and test designs.
  3. Probe for unidentified business needs and explore opportunities for team to develop appropriate, data-based approaches to those needs.
  4. Direct business-impacting team activities and articulate to senior management business plans and results of initiatives.
  5. Follow industry trends and test new tools and techniques to solve new/existing business challenges.

Skills

Required

  • 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)
  • Using source code control tools in a business environment such as Bitbucket or Github
  • Owning and leading an analytics workstream, including planning, project management, and resource management

What the JD emphasized

  • Selecting an artificial intelligence (AI) or machine learning (ML) approach for a given business problem
  • AI and ML modeling including supervised and unsupervised learning models and advanced analytics

Other signals

  • model development for complex business needs
  • business impact and robustness of solutions
  • AI and ML modeling including supervised and unsupervised learning models and advanced analytics