Data Scientist [multiple Positions Available]

JPMorgan Chase JPMorgan Chase · Banking · Plano, TX +1 · Consumer & Community Banking

Develop and apply statistical and mathematical models to analyze complex data trends and patterns, generate analytical insights, and develop statistical solutions that streamline business processes, uncover new opportunities, and bolster strategic decision-making. Conduct research on market trends and perform analysis of internal data. Utilize findings to craft actionable insights, guiding the strategic direction of the organization and identifying potential areas for product innovation and growth. Collaborate with cross-functional teams across product, business, and technology departments to implement data-driven strategies, in order to create and refine analytical models and algorithms. Document and communicate complex technical and analytical results tailored for a non-technical audience. Design and develop the analytical framework and algorithms for the automated application booking process for car dealers. Process monthly reporting of KPI, highlighting any anomalies in trend for senior leadership.

What you'd actually do

  1. Develop and apply statistical and mathematical models to analyze complex data trends and patterns using programming tools and data analysis software languages.
  2. Generate analytical insights and develop statistical solutions that streamline business processes, uncover new opportunities, and bolster strategic decision-making.
  3. Conduct research on market trends and perform analysis of internal data.
  4. Utilize findings to craft actionable insights, guiding the strategic direction of the organization and identifying potential areas for product innovation and growth.
  5. Collaborate with cross-functional teams across product, business, and technology departments to implement data-driven strategies, in order to create and refine analytical models and algorithms.

Skills

Required

  • data processing and ETL (Extract, Transform, Load) procedures
  • Machine learning (ML) scripting languages, including Python and R
  • ML algorithms such as logistic regressions, KNN, random forest, and Gradient boosting
  • private and public big data platforms, including AWS
  • statistical concepts for data analysis, including normal distribution, Bernoulli distribution, Bayesian inference, and sampling data
  • developing analytical solutions for business operations utilizing data analysis with statistical software such as R or Alteryx
  • presenting business insights and economic research results to a non-technical audience
  • using database systems such as Teradata, Oracle, or Snowflake
  • SQL for database querying and manipulating large datasets