Data Architect Principal

Works as part of a team to employ scientific methods and data-discovery tools to find new patterns, insights, and relationships in big data; extract meaningful information from multiple large data sets and develop solutions that quickly and visually communicate results. Participates and collaborates in discussions across business and IT teams to understand data product needs. Analyzes data from various databases to drive optimization and improvement of product development and business strategies.

Independently works on analyzing and interpreting data to identify trends, insights, and logical data driven solutions for business problems. Develops analytical models and algorithms which apply data to identify business improvement insights and inform the development of processes and tools (e.g. dashboards, other visualizations) for monitoring, analyzing, and evaluating analytical model performance. Ensures accuracy and quality of data, including reconciliation from disparate sources.

Partners with other team members to collaborate with stakeholders, customers, and other functional teams. Works alongside other data scientists to understand business questions, identify opportunities to leverage data to drive business solutions, implement models, and monitor outcomes.

Essential Functions

• Works as part of a team to perform advanced analytics, including predictive Machine Learning (classification and regression, supervised and unsupervised modeling), inferential statistics, hypothesis testing, and qualitative analysis on large data sets that include both structured and unstructured data to develop crucial business insights and communicate to the business in an understandable way.
• Work as part of a team to develop and deploy analytical and machine learning models (with general understanding of data engineering and ML Ops).
• Independently works on analyzing and interpreting data to identify trends, insights, and logical data driven solutions for business problems.
• Develops analytical models and algorithms which apply data to identify business improvement insights and inform the development of processes and tools (e.g. dashboards, other visualizations).
• Designs and implements reporting dashboards and other visualizations that track and visually communicate key business metrics and provide actionable insights.
• Participates in design sessions to implement experiments to answer targeted questions and solves known issues by actively engaging with business partners to understand key business challenges.
• Learn and become fluent working with and understand Fed Ex data.
• Identifies internal and external information sources, builds effective working relationships with subject matter experts within Fed Ex and the external marketplace.
• Assists with knowledge transfer and training of business intelligence to the Fed Ex team as a subject matter expert; strongly influences the team by developing, institutionalizing and maintaining a data-driven analysis culture.
• Analyzes data from various databases to drive optimization and improvement of product development and business strategies.
• Ensures accuracy and quality of data, including reconciliation from disparate sources.
• Performs other duties as assigned.

Minimum Education

Bachelor's in data science, computer science, applied mathematics, data analytics, statistics, industrial engineering, or related quantitative field.

Minimum Experience

Zero (0) to three (3) years relevant experience required. Skills can be gained concurrently with education. Proficiency required in at least one programming language, such as (SQL, Python, R, Java, or similar language) and one visualization tool/package such as (Spotfire, Tableau, Power BI, Shiny App, Plotly, Matplotlib, Seaborn or similar tool) required.

Knowledge, Skills, and Abilities

• Proficient in at least one programming language, such as (SQL, Python, R, Java, etc.).
• Proficient in one visualization tools/packages, such as (Spotfire, Tableau, Power BI, Shiny App, Plotly, Matplotlib, Seaborn). Exposure to relational database platforms, such as Teradata and Oracle.
• Experience in data mining, developing/implementing statistical models, machine learning models, algorithms, or visualizing data.
• Experience with statistical programming languages such as R, Python, SQL, and SAS required.
• Exposure to Machine Learning modeling, including scripting and Machine Learning libraries in R, Python, or similar.
• Exposure with Cloud platforms, including storage, querying, and advanced analytics (Azure, Google Cloud Platform, AWS).
• Understanding of statistics with exposure to linear algebra.
• Ability to define problems, collect data, establish facts, and draw valid conclusions.
• Ability to read, analyze, and interpret common technical journals, reports, and related documents.
• Exposure to data modeling, ETL (Extract-Transform-Load) concepts, query performance tuning and testing.
• Exposure to large datasets and the management of large data.
• Experience data modeling, ETL (Extract-Transform-Load)…