Traackr

• Establish tailored onboarding and training plans for new enterprise clients in APAC to ensure they learn, adopt, and are successful at the onset with the Traackr product. This includes specific coaching and use case analysis. • Determine critical measures for success for clients’ influencer programs to ensure there are quantitative and qualitative ways to measure progress. • Leverage knowledge of current important program gaps to elevate and highlight the Traackr product in addition to strategic program goals. • Monitor and address adoption and usage for clients in APAC region. • Partner with Client Partners and fellow Customer Success Managers to establish strategic account management efforts by identifying and escalating signals of risk accounts and areas for further account growth

• Improve reliability and correctness of a social platform integration, including data quality fixes • Expand and validate data ingestion to support additional attributes • Automate ingestion workflows and introduce monitoring for failures • Build API endpoints to support internal services • Develop internal tools and simple UIs for data access and visibility • Identify and track key user actions across services • Contribute to monitoring, alerting, and automation of system health

• Partner with Product and Engineering to identify high-impact opportunities, frame ambiguous problems, define success metrics, and choose pragmatic approaches (heuristics, statistics, ML, or GenAI). • Lead rigorous experimentation across teams: hypothesis design, metric/guardrail definition, power analysis, A/B testing (or quasi-experiments), and clear readouts that drive decisions. • Build and iterate on ML/AI capabilities that ship to production (e.g., classification, information extraction, ranking/recommendations, and GenAI components such as RAG or developing the agent harnesses for our core agentic journeys), optimizing for value-added, latency, and cost. • Establish best-in-class evaluation practices for both ML and LLM features: golden datasets, offline/online evaluation plans, regression suites, and monitoring that catches quality drift early. • Enable engineers to build safely and effectively with AI by coaching on prompt patterns, tool/function calling, structured outputs, guardrails, and debugging/evaluation workflows. • Design and support agentic workflows where they add real product value, with clear constraints, observability, and fallbacks. • Support the end-to-end lifecycle of deployed models and AI systems: data requirements, training/fine-tuning where relevant, validation, deployment, monitoring, incident response, and continuous improvement. • Raise org-wide leverage by creating reusable assets (evaluation harnesses, shared datasets, templates, documentation) and running enablement workshops. • Communicate insights and tradeoffs clearly to technical and non-technical stakeholders, turning analyses into decisions and measurable impact. • Champion responsible, privacy-aware AI: appropriate data handling, bias/fairness considerations where applicable, and human-in-the-loop workflows when needed.

Role Description The Senior Customer Success Manager will partner with our clients to optimize the implementation and adoption of Traackr, enabling teams to achieve their influencer program goals. We aim to deliver a best-in-class experience for every client at every touchpoint. You will make our customers real fans of our technology. - Establish tailored onboarding and training plans for new enterprise clients in APAC to ensure they learn, adopt, and are successful at the onset with the Traackr product. This includes specific coaching and use case analysis. - Determine critical measures for success for clients’ influencer programs to ensure there are quantitative and qualitative ways to measure progress. - Leverage knowledge of current important program gaps to elevate and highlight the Traackr product in addition to strategic program goals. - Monitor and address adoption and usage for clients in APAC region. - Partner with Client Partners and fellow Customer Success Managers to establish strategic account management efforts by identifying and escalating signals of risk accounts and areas for further account growth. Qualifications - 7+ years experience as a customer success manager working with global enterprise clients. - Experience with SaaS technology - preferably marketing or communication ecosystems, either directly or supporting a marketing product or a marketing technology. - Experience in beauty or influencer marketing. - Demonstrated public speaking and presentation skills. - Must have experience facilitating dynamic workshops consisting of content presentation and engaging exercises for both small and large groups of people. - Excellent written and verbal communication skills both internally and externally across multiple geographies. - Ability to speak with clients at all levels within the organization. - Collaborative and team-oriented mindset. You will be a true team player and know what it means to strive for your individual contribution while continuing to support your colleagues. - Experience working in a more consultative capacity with clients; comfortable running workshops, delivering presentations, and holding senior discussions about goals, work programs, and results. - Bachelor's degree or higher. - Fluent English, another APAC language is a plus. Benefits - Competitive Salary - Remote Work Options with Hybrid Flexibility and Home Office Set-Up Stipend - Coworking Office Subscription for Collaborative Spaces - Health, Dental, and Life Insurance Coverage* - Open Vacation Policy and Flexible Holiday Schedule to Suit Your Needs - Paid Parental Leave to Support Quality Time with Your Loved Ones - Career Development, including Internal and External Training Opportunities

• Own end-to-end revenue accounting for a high-growth SaaS business, ensuring compliance with ASC 606. • Own and maintain accurate ARR, MRR, churn, expansion, and related SaaS metrics, and ensure alignment between ARR, billings, and revenue. • Serve as the primary owner of Maxio, including billing setup, revenue schedules, reporting, and system integrations. • Own the full invoicing process, ensuring accuracy, timeliness, and alignment with customer contracts and pricing terms. • Manage customer collections, including AR aging, follow-ups, dispute resolution, and collaboration with Sales and Customer Success to drive timely cash collection. • Own vendor setup processes, ensuring accuracy, completeness, and compliance with internal controls and approval workflows. • Manage revenue, billing, and accounts receivable across multiple legal entities and currencies, including proper FX treatment. • Lead revenue-related activities for monthly, quarterly, and annual close, including reconciliations and journal entries. • Prepare revenue and AR schedules and serve as a key point of contact for external audits. • Review customer contracts in partnership with Legal, Sales, and Deal Desk to ensure proper revenue recognition and billing treatment. • Partner cross-functionally with FP&A, RevOps, Sales, Legal, and Customer Success to support forecasting, reporting, and operational decisions. • Identify and implement process improvements and automation opportunities to scale revenue, billing, and AR operations. • Maintain clear documentation, policies, and controls to support a SOX-ready and audit-ready environment

• Own end-to-end revenue accounting for a high-growth SaaS business, ensuring compliance with ASC 606. • Own and maintain accurate ARR, MRR, churn, expansion, and related SaaS metrics, and ensure alignment between ARR, billings, and revenue. • Serve as the primary owner of Maxio, including billing setup, revenue schedules, reporting, and system integrations. • Own the full invoicing process, ensuring accuracy, timeliness, and alignment with customer contracts and pricing terms. • Manage customer collections, including AR aging, follow-ups, dispute resolution, and collaboration with Sales and Customer Success to drive timely cash collection. • Own vendor setup processes, ensuring accuracy, completeness, and compliance with internal controls and approval workflows. • Manage revenue, billing, and accounts receivable across multiple legal entities and currencies, including proper FX treatment. • Lead revenue-related activities for monthly, quarterly, and annual close, including reconciliations and journal entries. • Prepare revenue and AR schedules and serve as a key point of contact for external audits. • Review customer contracts in partnership with Legal, Sales, and Deal Desk to ensure proper revenue recognition and billing treatment. • Partner cross-functionally with FP&A, RevOps, Sales, Legal, and Customer Success to support forecasting, reporting, and operational decisions. • Identify and implement process improvements and automation opportunities to scale revenue, billing, and AR operations. • Maintain clear documentation, policies, and controls to support a SOX-ready and audit-ready environment

• Design, build, and deploy scalable machine learning and AI systems in production environments. • Collaborate with Product Managers, Data Scientists and Engineers to lead the implementation of models and integrate them into data pipelines and agentic applications. • Lead model performance monitoring, retraining workflows, and continuous improvement. • Lead the implementation of data preprocessing and feature engineering pipelines for ML use cases. • Lead to experimentation and testing of AI models to improve accuracy and performance. • Develop, maintain, and optimize scalable data pipelines for analytics and machine learning workloads. • Ensure data reliability, quality, and performance across data systems. • Implement and maintain data ingestion pipelines from various internal and external sources. • Build and improve internal tools that support data operations and data quality. • Monitor and troubleshoot data pipelines to ensure consistent and timely delivery. • Contribute to improving platform efficiency and scalability. • Participate in code reviews and follow best practices in data and ML engineering. • Document data pipelines, ML workflows, and system architecture. • Contribute to evolving data and AI engineering best practices. • Stay current with emerging tools, frameworks, and trends in AI, ML, and data engineering.

• Tech lead two teams (DevEx and Cloud Infrastructure) totaling 6–8 engineers: set technical direction, review key designs/changes, and raise engineering standards across both domains. • Own the delivery toolchain end-to-end (Git, CI, deployments/releases): reduce flakiness, improve build/test times, make releases repeatable with clear rollback, and drive adoption of org-wide standards through tooling, docs, and supported migrations. • Own the reliability and scalability of Traackr's cloud infrastructure: keep production well-architected, cost-efficient, and resilient. Lead incident response and blameless postmortems; drive preventive improvements. • Improve the software development lifecycle (setup → build/test → PR → deploy → observe) and standardize environments so teams spend less time on tooling and more time shipping. • Identify high-value opportunities to integrate AI into developer workflows — e.g. code review, onboarding, or internal search — with sensible defaults around security and data handling. • Mentor engineers and raise the bar through documentation, pairing, and lightweight coaching.

• Own backend features end-to-end: discovery, design, implementation, rollout, and long-term reliability/operations • Design and evolve distributed systems (services, pipelines, data stores) with strong performance, scalability, and resiliency • Build and maintain APIs (REST/GraphQL, where relevant) and data access patterns supporting analytics and search use cases • Establish strong engineering fundamentals: code reviews, testing strategies, incident readiness, and operational excellence • Apply an experimentation-first approach: define hypotheses, define success metrics/guardrails, run controlled rollouts and A/B tests when appropriate, and write clear readouts for stakeholders. • Bring evaluation discipline to AI-assisted work: treat prompts/configs like versioned artifacts, design regression tests, measure quality changes, and monitor for drift (the same way you would for performance or correctness) • Use GenAI productively and responsibly (for coding assistance, debugging, and design reviews), while maintaining high standards for correctness, security, and privacy. • Mentor teammates and raise the bar through documentation, pairing, and lightweight coaching on best practices

• Develop, maintain, and optimize data pipelines for analytics and machine learning workloads. • Ensure reliability, scalability, and performance of data workflows. • Implement data ingestion from internal and external sources. • Collaborate with Data Scientists and Analysts to prepare datasets for modeling and analysis. • Build and maintain Databricks-based data pipelines and workflows. • Monitor, debug, and improve data pipeline performance and platform efficiency. • Develop tools and frameworks to automate data operations and ensure data quality. • Work with product and engineering teams to translate data requirements into solutions. • Participate in code reviews and follow data engineering best practices. • Maintain documentation and contribute to improving data engineering standards and tooling.