Data Scientist [multiple Positions Available]

JPMorgan Chase JPMorgan Chase · Banking · Jacksonville, FL +1 · Consumer & Community Banking

Data Scientist at JPMorgan Chase responsible for building, implementing, testing, and analyzing ML/AI products and solutions. The role involves leveraging statistical and mathematical techniques, including AI/ML and optimization, to solve business problems, designing controlled experiments, and translating business needs into data models. The position requires experience with SQL, pandas, data visualization libraries, and implementing traditional machine learning algorithms for predictive analytics.

What you'd actually do

  1. Build, implement, test, and analyze ML/AI products & solutions
  2. Conduct A/B testing, automate complex A/B reports, and demonstrate analytical thinking
  3. Convert data into actionable insights
  4. Leverage a variety of statistical and mathematical techniques and algorithms such as AI/ML, Optimization, time series analysis, or dimensionality reduction to analyze a variety of datasets to solve business problems
  5. Design controlled experiments to test and validate hypotheses

Skills

Required

  • Applying statistical analysis and predictive modeling techniques to drive strategic decision-making and business outcomes
  • Creating data visualizations and dashboards to uncover actionable insights that guide business strategy and operations
  • Writing SQL queries including Common Table Expressions, window functions, dynamic SQL, and joins across database platforms including MySQL and PostgreSQL
  • Manipulating and analyzing datasets using pandas
  • Creating data visualizations with Matplotlib and Seaborn
  • Developing interactive analytical tools with Jupyter Notebooks
  • Designing and implementing automated data collection systems to support A/B testing initiatives
  • Generating performance reports using Python libraries including SciPy, Statsmodels, and NumPy
  • Applying optimization techniques and algorithms to resolve operational challenges and improve business processes
  • Implementing traditional machine learning algorithms and methodologies for predictive analytics and data-driven insights

What the JD emphasized

  • Applying statistical analysis and predictive modeling techniques to drive strategic decision-making and business outcomes
  • Implementing traditional machine learning algorithms and methodologies for predictive analytics and data-driven insights

Other signals

  • Build, implement, test, and analyze ML/AI products & solutions
  • Leverage a variety of statistical and mathematical techniques and algorithms such as AI/ML, Optimization, time series analysis, or dimensionality reduction to analyze a variety of datasets to solve business problems
  • Translate business needs into data models supporting long- term solutions