Operations Research / Data Platform Engineer

Key Responsibilities

• Formulate and apply mathematical and statistical models to evaluate the performance of NexusOne's cross-estate data orchestration layer, identifying optimization opportunities across identity management, governance enforcement, and data pipeline execution.
• Define data requirements, gather and validate quantitative information from NexusOne's operational environment, and apply statistical methods to assess platform performance, pipeline throughput, and governance policy compliance across client deployments.
• Present the results of mathematical modeling and data analysis to client stakeholders and delivery leadership, translating quantitative findings into platform configuration recommendations and operational decisions.
• Collaborate with engineering and client delivery teams to identify and solve complex data operational problems — including legacy system modernization, AI pipeline readiness, and cross-estate governance gaps — using NexusOne as the enabling platform.
• Prepare technical and management reports evaluating data operational problems, analyzing solution alternatives, and recommending NexusOne configurations that meet client performance, compliance, and AI readiness requirements.

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, Business Analytics, or a related field that develops analytical, logical, reasoning, and problem-solving skills.
• Experience in the information technology industry applying quantitative and analytical methods to operational or data platform challenges.
• Working knowledge of statistical analysis and mathematical modeling techniques as applied to data systems and platform performance evaluation.
• Ability to translate quantitative findings into clear recommendations for both technical and non-technical stakeholders.

Preferred Qualifications

• Master's degree in Business Analytics or a related discipline.
• Experience transforming legacy infrastructure into scalable Spark/Airflow environments to support real-time analytical workloads.
• Experience delivering data products that reduce operational latency and cost while enabling data-driven decision-making.
• Familiarity with Infrastructure as Code practices supporting process optimization and automation.