Team Lead - Service Delivery

The CoinDCX Journey: Building Tomorrow, Today

• CoinDCX is India’s largest and safest cryptocurrency exchange, focused on
• making crypto accessible through simple, secure, and innovative solutions.

• Established in 2018, CoinDCX provides a diverse suite of crypto-based
• financial products and services for retail, HNI, and enterprise customers. The
• platform enables users to build and manage their cryptocurrency portfolios
• while providing access to advanced products and trading solutions.

• CoinDCX is backed by leading global investors including Coinbase Ventures,
• Polychain Capital, Bain Capital Ventures, Block.one, and HDR Group, the
• operator of BitMEX. CoinDCX is also insured by BitGo and ISO certified.

Inside CoinDCX’s Customer Success Team

• Our Customer Success team is dedicated to ensuring our users get the most out
• of their CoinDCX experience. We build strong relationships, providing support
• and guidance to help users achieve their goals in the world of digital finance.
• If you’re passionate about customer satisfaction and success, join us in
• creating exceptional experiences that turn users into advocates.

• 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, CoinDCX 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 LOB's 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.

You need to be a HODLer of these

Must Have

• * 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 judgment 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.

You will be mining through these tasks

Team Leadership

• * 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 environment 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. AI-Augmented Operations Management

• * 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 (for example, AI
• suggested tolerance calls that examiners or agents later overturn), 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. Quality Assurance

• * 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. Data Analysis and Reporting

• * Generate regular reports on key performance metrics such as response times,
• resolution rates, escalation rates, and customer satisfaction scores.
• * Own the LOB's 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 —
• moving from 'CSAT dropped' to 'CSAT dropped because X, and here is the
• proposed fix'.
• * Distinguish signal from noise in performance data: separate a genuine,
• systemic issue (for example, a price-feed timing problem spanning multiple
• instruments) from a localised one (for example, liquidity depth