Data Scientist

Data Scientist Role Summary

One Magnify'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 support are making high‑stakes decisions about customers, markets, and products. Your models—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 will also contribute to building the analytics capabilities One Magnify delivers at scale, writing code and documentation that others can reproduce, maintain, and extend.

Shipping a model is the beginning, not the end.

• 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, 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 by partnering 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, ensuring 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 using Git/Git Lab 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/Git Lab 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.