Technology Operational Effectiveness Analyst

Role Overview

Verition's technology team supports seven investment strategies and the enterprise functions behind them. This role exists to continuously evaluate how effectively the technology organization is operating and identify actions that improve delivery, reliability, efficiency, unit economics, and AI leverage across the estate.

Reporting to the Technology COO, this individual will work across the entire technology function to identify where time, cost, and quality are being lost, quantify the impact, and deliver recommendations with clear ownership and measurable outcomes. This is not a reporting-focused role; AI generates the reporting layer, allowing the individual to focus on analysis, judgment, and driving change.

Responsibilities
Own the engineering intelligence stack, including delivery velocity, software reliability, service performance, onboarding effectiveness, and AI-assisted development metrics.
Develop and maintain live unit economics across technology, including cost per pod, workload, transaction, and AI workflow.
Analyze consumption spend across AI inference, cloud infrastructure, and market data platforms.
Identify structural inefficiencies, quantify their impact, and drive remediation efforts to closure.
Provide recommendations across the technology organization, including prioritization, sequencing, approval, and rejection decisions supported by measurable outcomes.
Conduct internal and external benchmarking of technology platforms, tooling, services, and operating practices.
Determine where instrumentation and measurement provide actionable decision-making value and avoid unnecessary reporting.
Build and leverage AI-driven agents, pipelines, and automation to collect, reconcile, and synthesize technology data.
Utilize AI tools to generate reporting and briefing materials, allowing focus on analysis and strategic decision-making.
Partner closely with engineers, Vertical COOs, and technology leaders to build trust, communicate findings, and drive adoption of recommendations.
Present analyses and recommendations clearly, concisely, and with defined ownership and expected business impact.

Qualifications

2-4 years of experience at a top-tier strategy consultancy or a quantitative analytical background in engineering, the sciences, or quantitative research within a leading financial services organization.
Strong proficiency in Python.
Experience working with telemetry, logs, cost data, APIs, dashboards, and operational technology metrics.
Demonstrated analytical and quantitative problem-solving skills, with the ability to size opportunities, defend assumptions, and evaluate tradeoffs.
Ability to identify meaningful metrics and translate analysis into actionable recommendations.
Experience leveraging AI tools, automation, or agents to improve workflows, generate insights, and increase operating efficiency.
Strong communication and stakeholder management skills
Ability to challenge and defend positions through data-driven reasoning while remaining open to alternative perspectives.
Financial services experience is beneficial but not required.
Curiosity about technology platforms and how trading systems operate.
Demonstrated track record of ownership and execution.
Excellent written and verbal communication skills.
High level of intellectual curiosity, strong work ethic, and keen attention to detail.
Ability to work effectively in a team-oriented, fast-paced, and dynamic environment.