Equity Research Expert

Xperteez Technology is seeking an Equity Research Expert for a contract engagement. The role centers on delivering high-fidelity equity research inputs to train next-generation AI systems, turning domain knowledge into model-ready guidance. The position is hybrid and based in International City, AE, blending independent analysis with collaborative sessions across project teams. If you’ve spent years building accurate models, conducting earnings analyses, and benchmarking peers, you’ll likely fit this team well.

No prior AI background is required, the emphasis is on your sector know-how and your ability to translate that expertise into precise, real-world data and narratives for AI training. You’ll help shape how models learn and reason through the inputs you provide, keeping the human insight you bring at the core of the work.

The role suits someone who has owned company models end to end, contributed to published research, and regularly covers multiple names through several earnings cycles. You’ll bring deep sector insights, strong written and verbal communication, and a track record of turning complex financial signals into clear, actionable perspectives.

Day-to-day impact in a hybrid equity research role

Your daily work centers on reviewing datasets that model real-world equity research, ensuring accuracy and relevance for AI training. You’ll dissect earnings reports, revise estimates, and distill peer benchmarking insights to feed model learning, translating numbers into meaningful input for AI systems.

In addition, you’ll deliver thorough written evaluations of company models, including revenue dynamics, margins, cash flow, and the drivers of value, while producing concise research summaries and event-driven updates grounded in your sector expertise. You’ll craft investment theses that weave qualitative signals with quantitative factors to yield actionable perspectives, and you’ll keep an eye on industry trends, sector metrics, and catalysts that inform fundamental analysis.

Collaboration is essential: you’ll work with project stakeholders to provide feedback on data quality and help enhance research content so it’s better suited for training and evaluation tasks.

The must-have pieces of the puzzle

• Extensive experience as a sell-side Research Associate with direct ownership of company models and published research, or in a senior publishing role such as VP or Director
• Proven track record covering multiple companies through several earnings cycles while maintaining solid, comprehensive models
• Ability to link business drivers to financial outcomes and the resulting valuation implications
• Specialist sector knowledge in at least one area (for example banks, insurance, biotech) with the capacity to share deep insights
• Exceptional written and verbal communication that helps others grasp complex financial ideas
• Experience performing granular earnings analysis, peer benchmarking, valuation work, and event-driven research updates
• Experience formulating and defending investment theses and research views, with accountability for accuracy and quality

Nice-to-have capabilities

• Past roles as a lead researcher or senior analyst with ownership of models and published research, including titles such as VP or Director
• History of independently covering a portfolio of companies across multiple earnings cycles with robust models
• Strong ability to map business dynamics to financial outcomes and fair value
• Sector depth in banks, insurance, biotech, or related areas with clear, actionable insights
• Excellent written and verbal communication, able to explain complex concepts clearly
• Background in earnings analysis, peer benchmarking, valuation, and event-driven updates at a granular level
• Experience defending investment theses and research viewpoints with a focus on data quality and accuracy

Compensation and the practical basics

This is a contract role with a hybrid work setup based in International City, AE.

Tips to position yourself for this role

Lead your resume with your equity research leadership, open with your ownership of company models and published research, and call out any titles like VP or Director if applicable.

Back up the must-have skills with concrete examples: describe the earnings cycles you’ve covered, how you revised estimates, and how you tied business drivers to valuation in a way that shows clear, auditable impact.

Be ready to discuss investment theses and research views in interviews, and bring samples of past updates that demonstrate how you communicate complex ideas and support conclusions with data.

During discussions, ask a sharp question about data and training: how the team translates your research into AI training inputs and what data quality standards guide that work.