Data Scientist

OneMagnify's Data Scientists sit at the intersection of client strategy and technical delivery, turning complex business questions into models, analyses, and insights that clients actually use to make decisions. You'll work alongside Data Engineering, AI, and cross-functional teams to design and deploy solutions that span the full analytics lifecycle, from data integration and quality to predictive modeling and advanced analytics. This role is a fit for someone who wants to do serious technical work and see it matter in the real world.

The clients you'll support are making high-stakes decisions about customers, markets, and products. Your models, including forecasting demand, segmenting audiences, and optimizing spend, become the analytical backbone of how they operate. When your work is right, it drives measurable outcomes. When it's wrong, someone notices. That accountability is part of what makes this role interesting.

You'll also contribute to building the analytics capabilities OneMagnify delivers at scale. That means writing code and documentation that others can reproduce, maintain, and extend. Shipping a model is the beginning, not the end. Cross-functional collaboration with engineering, strategy, and delivery teams is part of the daily rhythm, and your ability to translate between technical and business contexts will be used constantly.

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.

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

• 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

• 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