Data Scientist Jobs

About The Position

Requirements

• 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

Nice To Haves

• 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
• Familiarity with Master Data Management (MDM) concepts and how they apply to data quality and integration

Responsibilities

• 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