Data Scientist

OneMagnify is an AI native, platform-enabled B2B digital agency operating at the intersection of data, technology, and creativity. We help complex organizations drive measurable business outcomes by building smarter customer experiences and delivering highly integrated solutions across digital, media, and technology. By combining deep industry expertise with advanced analytics and artificial intelligence, we enable our clients to make better decisions, move faster, and compete more effectively in dynamic markets. 

Role Summary

As a Data Scientist at OneMagnify, you'll translate complex business problems into analytical solutions that directly shape how clients understand their customers and make decisions. This is a craft-focused role sitting at the intersection of data engineering, advanced analytics, and client delivery — where your models and insights show up in real products and real business outcomes. You'll work closely with data engineering, analytics, and client teams to make sure the work you build is rigorous, well-documented, and built to last.

The Impact You'll Have

The analyses and models you build here don't live in a sandbox — they inform how clients in automotive, B2B, and industrial sectors run campaigns, prioritize customers, and allocate resources. A segmentation model you develop might shape how a manufacturer targets their dealer network. A forecasting solution you deploy might drive a client's quarterly planning. The work is technical, but the outcomes are concrete.

You'll work across a range of analytical challenges: predictive modeling, experimentation, optimization, and KPI development — often in the same engagement. That breadth means you'll grow your skills quickly and develop a strong intuition for when different approaches are the right fit. You'll also work hands-on in Databricks, building scalable pipelines and contributing to MLOps practices alongside engineering teams.

Beyond the modeling work, you'll be expected to communicate what you find. That means building executive-ready visualizations in Tableau or Power BI, presenting to both technical and business audiences, and translating analytical complexity into decisions clients can act on.

What You'll Do

Build and Deploy Analytical Models

• Design, develop, and validate predictive models — forecasting, classification, regression, and segmentation — with rigorous diagnostics and clear documentation
• Conduct A/B testing and causal analyses to support experimentation programs and measure the impact of client initiatives
• Develop optimization solutions (linear, mixed-integer, multi-objective) and ensure models are production-ready with monitoring in place

Work with Data at Scale

• Use Databricks for large-scale data processing and machine learning workflows, integrating data from diverse sources across complex client environments
• Monitor and validate data flows, develop data quality reporting, and perform root-cause analysis when issues surface
• Collaborate with data engineering teams on MLOps practices — MLflow pipelines, model deployment, lifecycle management, and version control via Git/GitLab

Turn Analysis into Decisions

• Execute advanced analyses, synthesize findings, and build executive-ready presentations and visualizations using Tableau and Power BI
• Develop new metrics and KPI reports that give clients and internal stakeholders a clearer view of business performance
• Communicate effectively with both business and technical audiences — adapting your framing to the room

Partner with Business and Engineering Teams

• Work with business partners and data engineering teams to elicit requirements, define business rules, and translate client needs into technical specifications
• Contribute to project meetings to align on scope, timelines, and analytical approach across multi-member engagements
• Test database changes prior to release and ensure data designs align with defined standards and governance requirements

Ensure Data Governance and Compliance

• Apply data governance, privacy, and compliance standards consistently across analytical work
• Document models, processes, and business rules clearly enough that others can reproduce, validate, and build on your work

What You'll Need

• Bachelor's degree in Computer Science, MIS, Statistics, Mathematics, Marketing Research, or a related quantitative field — or equivalent practical experience
• 2–5+ years of hands-on analytics experience including predictive modeling, A/B testing, and optimization
• Advanced SQL and Python for querying, manipulating, and interpreting data from databases and data warehouses
• Hands-on experience with Databricks for large-scale data processing and machine lear