The work spans industries and problem types (automotive, retail, financial services, and more) so you'll develop breadth alongside depth. You'll rarely work on the same type of problem twice in a row.
What You'll Do
Build and validate analytical models
Design, deploy, and monitor models including forecasting, classification, regression, and segmentation
Conduct A/B testing and causal analyses with rigorous experimental design and clear documentation
Develop optimization solutions (linear, mixed-integer, multi-objective) and ensure reproducibility across the full model lifecycle
Own data integration and quality
Integrate data from multiple sources and develop data-quality reporting that surfaces issues before they become client problems
Conduct root-cause analysis on data anomalies and validate database changes prior to release
Use Databricks for large-scale data processing and machine learning workflows
Translate requirements into technical solutions
Partner with business and engineering teams to elicit requirements, define business rules, and turn them into technical specifications
Document solutions clearly enough that someone else can maintain and extend your work
Ensure alignment between what clients ask for and what gets built
Communicate findings to varied audiences
Synthesize and present analytical findings to internal and external stakeholders, including executive-level audiences, with the judgment to handle complex or sensitive inquiries with care
Build metrics and KPI reports that inform real business decisions, not just dashboards that get ignored
Prepare visualizations in Tableau and Power BI that make complex outputs accessible
Support collaborative development
Use Git/GitLab for version control, reproducibility, and collaborative code development
Collaborate with engineering teams to implement MLOps practices including model deployment, monitoring, and end-to-end lifecycle management using tools such as MLflow
Adhere to data governance, privacy, and compliance standards across all work
What You'll Need
BA/BS in Computer Science, Statistics, Mathematics, MIS, Marketing Research, or a related quantitative field — or equivalent practical experience
2–5+ years of hands-on analytics including predictive modeling, A/B testing, and optimization
Advanced SQL and Python; strong ability to query, manipulate, and interpret data from databases and data warehouses
Hands-on experience with Databricks for large-scale data processing and machine learning workflows
Proficiency with Tableau and/or Power BI for visualization and reporting
Experience with Git/GitLab for version control and collaborative development
Strong Excel and PowerPoint skills
Proven ability to present analyses to management and collaborate with both business and technical stakeholders
Experience diagnosing and resolving data-quality issues across multiple platforms
Understanding of data governance, privacy, and compliance standards
Familiarity with Master Data Management (MDM) concepts and how they apply to data quality and integration
Future-Ready Skills (Nice to Have)
Proficiency with SAS or R in an applied analytics environment
Familiarity with automotive or VIN data and complex industry-specific data structures
Exposure to AI-enabled analytics workflows or automation within a data science context
Experience working in integrated marketing, consulting, or digital services environments where analytics supports client-facing delivery
