Key Responsibilities: The Senior Analyst - Cargo Business Intelligence is a high-impact individual contributor within United Cargo's Logistics division, responsible for designing analytical frameworks that transform operational data into strategic intelligence and enable proactive risk mitigation. Reporting to the Senior Manager - Cargo Business Intelligence & Revenue Recovery, this role serves as the division's analytical center of excellence, leveraging Python, SQL, Power BI, Spotfire, Alteryx, and Salesforce CRM to build enterprise-grade dashboards, predictive models, and automated reporting pipelines. This position delivers actionable insights that optimize performance across Claims, Customer Resolution, and Revenue Recovery functions, supporting United Cargo's $1.7B revenue stream through data-driven decision-making and operational foresight.
Strategic Analytics and Proactive Risk Intelligence - Design and execute advanced analytical solutions that identify leading indicators of operational risk, including claims exposure, dispute escalation, collections deterioration, and service failures.
- Develop and validate predictive models using Python, SQL, and statistical techniques (regression analysis, time series forecasting, anomaly detection, multi-variate segmentation).
- Translate complex analytical outputs into executive-ready narratives and strategic recommendations that inform resource allocation and process redesign.
- Serve as analytical thought partner to senior leadership on strategic initiatives, business case development, and performance improvement opportunities.
Business Intelligence and Reporting - Design and maintain enterprise-grade dashboards and operational reporting frameworks using Power BI, Spotfire, and Salesforce CRM.
- Establish and govern KPI definitions, metric frameworks, and SLA reporting standards across Cargo Logistics functions.
- Design data visualizations that balance analytical rigor with executive accessibility, enabling action without technical interpretation.
- Proactively surface anomalies, trend deviations, and emerging risk signals to senior leadership.
Data Engineering, Automation and Pipeline Development - Design and maintain automated data pipelines and workflows using Alteryx and Python for scalable, repeatable data preparation.
- Write and optimize complex SQL queries and stored procedures for data extraction, transformation, and analysis across relational databases.
- Integrate disparate operational data sources (Salesforce CRM, financial systems, cargo platforms, third-party feeds) into unified analytical environments.
Predictive Modeling and Operational Prevention - Develop and maintain machine learning models for demand forecasting, claims frequency prediction, customer churn risk scoring, and process failure detection.
- Validate, monitor, and retrain models to ensure sustained accuracy, applying Ops principles for production readiness and explainability.
- Operationalize model outputs within Salesforce workflows, Alteryx pipelines, and reporting environments to enable preemptive intervention.
Note: This is a Chicago-based hybrid role with in-office requirements 50% of the time.
Qualifications What's needed to succeed (Minimum Qualifications): - Bachelor's degree in Economics, Finance, Data Analytics, Statistics, Mathematics, Computer Science, Engineering, or related analytical field
- 2+ years of experience in an analytical role in business operations, finance, logistics, or transportation environment
- Experience developing automated reporting pipelines and analytical workflows in a complex, multi-stakeholder environment
- Experience using enterprise BI tools (Power BI, Spotfire) for dashboard design, KPI governance, and reporting
- Demonstrated proficiency with SQL, Python, and at least one enterprise BI platform (Power BI, Spotfire, or Tableau)
- Knowledge of advanced data analytics methodology, statistical modeling, predictive analytics, machine learning, and data visualization
- Deep proficiency with SQL for data extraction, transformation
- Strong working knowledge of Python for data manipulation (Pandas, NumPy), statistical analysis, and machine learning model development
- Familiarity with Alteryx for workflow development and analytics automation
- Exceptional analytical reasoning with ability to synthesize complex datasets into executive-ready strategic intelligence
- Strong written, verbal, and presentation skills with experience presenting to senior leadership
- Ability to operate independently, driving high-impact deliverables in fast-paced environment
- Cross-functional collaboration and influence skills across Finance, Sales, Operations, Digital Technology, and Accounting
- Strategic prevention mindset with ability to identify leading operational risk indicators
- Must be legally authorized to work in the United States for any employer without sponsorship
- Successful completion of interview required to meet job qualification
- Reliable, punctual attendance is an essential function of the position
What will help you propel from the pack (Preferred Qualifications): - Master's degree in Data Science, Business Analytics, Applied Mathematics, Operations Research, or related field
- Six Sigma Certification
- Lean Six Sigma Certification
- Industrial Engineering Qualification / Certification / License
- 5+ years of experience with advanced analytics
- Experience building and deploying predictive models in a production business environment
- Experience using Alteryx, Salesforce CRM analytics, or Databricks
- Experience in cargo, logistics, airline, or supply chain operations
- Experience with Data Ops and version control practices for governed, reproducible analytics workflows
- Knowledge of salesforce CRM data structures and reporting capabilities in operations or logistics context
- Knowledge in ML Ops principles including model validation, monitoring, retraining, and explainability frameworks
- Knowledge of cargo logistics operations, air cargo commercial frameworks, and supply chain risk management
- Knowledge in data governance, data quality management, and enterprise data architecture principles
- Advanced data visualization skills with ability to design dashboards for non-technical leadership audiences
- Meticulous attention to data accuracy, documentation standards, and analytical reproducibility
- Proficiency with AI-augmented analytics tools and prompt engineering techniques
- Demonstrates an understanding of the capabilities and limitations of generative AI tools and effectively leverages them to support research, analysis, content creation, and problem-solving. Apply critical thinking to validate AI-generated outputs while adhering to organizational policies for responsible AI use, data privacy, and security.
