Job Summary

As Team Lead for Trade Customer Success, you own the daily operations of a team resolving customer queries across Spot, Futures, Options, Order-related issues, Trading & TDS, Web3, US Stocks, and Crypto Deposits/Withdrawals, in an AI-augmented environment. Your team works alongside AI systems that draft responses, assess execution-price disputes against defined tolerance thresholds, and surface knowledge; your job is to ensure the humans and the AI together produce accurate, fair, and risk-aware outcomes, particularly on cases with direct financial impact such as stop-loss and take-profit non-triggers.

You lead through data: performance conversations, coaching decisions, and operational calls are grounded in dashboards, instrument-level complaint analysis, and trend evidence rather than intuition. While ownership of the external agency sits with the Agency Manager, you remain the LOBs insight authority, sharing SOP updates, execution-dispute trends, and quality learnings with external agents and supporting the Agency Manager with LOB-level context whenever needed.

Responsibilities

- Provide guidance to the team of customer support experts within the internal team, setting clear goals and tracking progress toward achievement.

- Foster a positive and supportive work workplace that encourages teamwork, collaboration, and personal development.

- Conduct regular one-on-one meetings to support individual growth and performance improvement, using performance data such as quality scores, productivity metrics, and AI-assist indicators as the foundation for coaching conversations.

- Coach agents on AI tool judgment: when to approve an AI-suggested slippage-tolerance classification, when to override it, and how to write correction notes that improve system accuracy on execution-dispute cases.

- Listen to team members feedback and resolve any issues or conflicts.

- Undertake additional responsibilities as needed to contribute effectively to the team's success.

- Monitor real-time queue health, agent utilisation, and SLA thresholds through AI-powered dashboards, intervening before breaches occur rather than reporting them after.

- Review AI accuracy reports for the LOB regularly: identify misclassification patterns on execution-dispute cases, recurring low-confidence categories, and systemic AI failures, and escalate them with supporting evidence.

- Own the human-AI workload balance for the team: ensure AI-flagged high-risk tickets - particularly stop-loss and take-profit non-triggers - are routed to the right seniority level immediately, and that no agent is either overloaded or underutilised.





- Validate that case labels and execution-tolerance classifications applied by the team (and pre-filled or suggested by AI) are accurate, since this data feeds every downstream analysis, partner-exchange escalation, and automation decision.

- Implement and maintain quality assurance processes to ensure customer interactions meet company standards and expectations.

- Monitor and evaluate chat and ticket transcripts - including AI-assisted interactions - to identify areas for improvement and provide feedback to agents.

- Work closely with agents to implement training programs and resources that enhance skills and knowledge.

- Discover training needs, provide coaching, recognise high performance, and reward accomplishments.

- Encourage creativity and considered risk-taking within quality and compliance boundaries.

- Generate regular reports on key performance metrics such as response times, resolution rates, escalation rates, and customer satisfaction scores.

- Own the LOBs execution-dispute driver analysis: identify which instruments or trading pairs are generating disproportionate complaint volume relative to order flow, and what that means for staffing, training, or partner-exchange escalation.

- Perform root cause analysis on recurring issues and negative trends and propose fixes.

- Distinguish signal from noise in performance data; present regular LOB insight summaries covering performance trends, AI accuracy observations, and recommended actions.

- Make recommendations for operational enhancements or resource allocation based on data-driven insights.

- Share SOP updates, process changes, and knowledge base corrections with external agency teams so frontline chat agents always work from current information.

- Surface LOB-level insights - recurring execution-dispute patterns, emerging contact drivers, quality patterns - to the Agency Manager to inform vendor coaching and performance conversations.

- Support the Agency Manager with LOB context during vendor reviews and quality discussions.

- Ownership of the agency relationship sits with the Agency Manager; insight authority sits with you.

Requirements

- 5+ years of experience in the service industry, with at least 2 years as a team leader or supervisor.

- In-depth knowledge of contact centre performance metrics - SLA, response times, resolution rates, CSAT,



escalation rate - and how they interconnect.

- Demonstrated ability to interpret data trends and translate them into operational actions: read a dashboard, identify what changed and why, and decide what to do about it.

- Ability to perform structured root cause analysis, moving beyond symptoms to underlying causes across process, people, product, and partner dimensions.

- Ability to coach and develop agents using evidence: turning performance data into specific, actionable coaching conversations.

- Comfortable working with percentages and thresholds under time pressure - for example, calculating whether an execution price falls within an agreed slippage tolerance band.

- Working knowledge of order-execution and trading-support flows - order types, execution disputes, stop-loss/take-profit mechanics - from a brokerage, trading, fintech, or similar regulated environment, with the ability to quickly learn crypto-exchange specifics.

- Excellent communication and leadership skills, with the ability to present insights and recommendations clearly to senior and cross-functional stakeholders, including Trading Ops and Partner Exchange Ops.

- Strong command of MS Excel and dashboard or reporting tools.

- Organisational, time-management, and decision-making skills, including sound judgement under pressure with incomplete information.

- Willingness to work in rotational shifts including weekends and public holidays.

- Preferred experience working in an AI-assisted or AI-augmented support environment, including familiarity with AI-drafted responses, auto classification, or confidence-score-based workflows.

- Understanding of order-execution taxonomy or contact driver frameworks and why classification accuracy matters for downstream analysis and partner-exchange escalation.

- Exposure to derivative products such as Futures or Options, and coordination with third-party liquidity providers or exchange partners.

- Familiarity with Sprinklr, Zendesk, Freshdesk, or equivalent CRM reporting environments.

- Understanding of quality assurance methodology: sampling, calibration, and structured feedback.

- Degree in Management or formal training in team leadership.

Qualifications

Preferred Experience

- Experience working in an AI-assisted or AI-augmented support environment, including familiarity with AI-drafted responses, auto classification, or confidence-score-based workflows.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Team Lead - Service Delivery (Mumbai)
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