Data Scientist

Data ScientistOneMagnify'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.What You'll DoBuild and validate analytical modelsDesign, deploy, and monitor models including forecasting, classification, regression, and segmentationConduct A/B testing and causal analyses with rigorous experimental design and clear documentationDevelop optimization solutions (linear, mixed-integer, multi-objective) and ensure reproducibility across the full model lifecycleOwn data integration and qualityIntegrate data from multiple sources and develop data-quality reporting that surfaces issues before they become client problemsConduct root-cause analysis on data anomalies and validate database changes prior to releaseUse Databricks for large-scale data processing and machine learning workflowsTranslate requirements into technical solutionsPartner with business and engineering teams to elicit requirements, define business rules, and turn them into technical specificationsDocument solutions clearly enough that someone else can maintain and extend your workEnsure alignment between what clients ask for and what gets builtCommunicate findings to varied audiencesSynthesize and present analytical findings to internal and external stakeholders, including executive-level audiences, with the judgment to handle complex or sensitive inquiries with careBuild metrics and KPI reports that inform real business decisions, not just dashboards that get ignoredPrepare visualizations in Tableau and Power BI that make complex outputs accessibleSupport collaborative developmentUse Git/GitLab for version control, reproducibility, and collaborative code developmentCollaborate with engineering teams to implement MLOps practices including model deployment, monitoring, and end-to-end lifecycle management using tools such as MLflowAdhere to data governance, privacy, and compliance standards across all workWhat You'll NeedBA/BS in Computer Science, Statistics, Mathematics, MIS, Marketing Research, or a related quantitative field — or equivalent practical experience2–5+ years of hands-on analytics including predictive modeling, A/B testing, and optimizationAdvanced SQL and Python; strong ability to query, manipulate, and interpret data from databases and data warehousesHands-on experience with Databricks for large-scale data processing and machine learning workflowsProficiency with Tableau and/or Power BI for visualization and reportingExperience with Git/GitLab for version control and collaborative developmentStrong Excel and PowerPoint skillsProven ability to present analyses to management and collaborate with both business and technical stakeholdersExperience diagnosing and resolving data-quality issues across multiple platformsUnderstanding of data governance, privacy, and compliance standardsFamiliarity with Master Data Management (MDM) concepts and how they apply to data quality and integrationFuture-Ready Skills (Nice to Have)Proficiency with SAS or R in an applied analytics environmentFamiliarity with automotive or VIN data and complex industry-specific data structuresExposure to AI-enabled analytics workflows or automation within a data science contextExperience working in integrated marketing, consulting, or digital services environments where analytics supports client-facing delivery