Why This Role, Why Now

GTM Data Strategy & Operations stood up from scratch with no predecessor. Today the function runs on three offshore contractors and zero FTEs, managed by a single leader who is simultaneously building the agentic infrastructure, operating it in production, and driving major initiatives (hierarchy redesign, data quality assessment, vendor optimization).

The operating model is deliberately agentic AI–first: a multi-agent pipeline (Cartographer, Sentinel, Resolver, Reporting) handles detection, enrichment, hierarchy mapping, and conflict resolution at scale. This is not a future-state vision, these agents are live and processing enterprise account families in production today.

The problem: one person cannot build, operate, and extend this system while also managing strategic workstreams. The function currently covers only core Tier‑1 fields. Dozens of account, contact, and lead signals remain unaddressed. Every pipeline run, every failure diagnosis, and every offshore handoff flows through a single point of failure.

This role is the first onshore execution hire for an agent operator who can keep the system running, improve it, and extend detection and resolution coverage as GTM leadership prioritizes new data elements.

Role Summary

Sit between AI systems and GTM data. Operate, tune, and extend our agentic data quality pipeline (detection, enrichment, hierarchy mapping, conflict resolution) so it runs reliably, improves continuously, and expands to cover more of the data landscape. Own the handoff between automated output and human review, managing quality and throughput with our offshore team. You don’t build agents from scratch, but you run them, evaluate their output with GTM data judgment, and make them better.

Core Responsibilities

Agent Pipeline Operations

• Run and monitor production pipeline sessions (Cartographer, Sentinel, Resolver) across scheduled cadences; diagnose and resolve failures (API errors, session timeouts, data anomalies) without escalating to the function lead.
• Execute pipeline runs in Claude Claude and tmux; manage long-running batch processes; interpret logs and output to confirm data integrity before downstream handoff.
• Maintain pipeline orchestration scripts and configuration; extend agent coverage as new data elements are prioritized by GTM leadership.

Agent Tuning & Improvement

• Refine detection rules, prompt logic, and confidence thresholds based on output analysis and false-positive/negative patterns.
• Evaluate agent accuracy by segment (Enterprise vs. MM/SMB) and recommend rule or workflow changes backed by evidence.
• Run bake-offs (vendor vs. AI enrichment) to optimize cost, coverage, and accuracy; document results for decision-making.

Sentinel → Offshore Resolution Loop

• Own the handoff between Sentinel detection output and Concentrix triage queues; define queue structure, priority tiers, and resolution instructions.
• Monitor offshore resolution quality and throughput; refine detection rules based on patterns surfaced through triage.
• Close the feedback loop: track resolution outcomes back to agent configuration to reduce recurring false positives and improve detection precision.

Data Quality & Enrichment Operations

• Maintain ops-only staging fields; manage the promote-to-production flow with audit controls.
• Design and run AI-assisted enrichment workflows (Clay + LLM prompts) with evidence links and confidence thresholds.
• Monitor fill-rate, sampled accuracy, freshness, and cost-per-record by source and segment; surface vendor performance issues and recommend changes.
• Keep data dictionaries, SOPs, and runbooks current as agents and processes evolve.

Cross-Functional Partnership

• GTM Systems (SFDC): field configuration, permission sets, automation, flows.
• Data Engineering: source availability, ID mapping, lineage (no pipeline coding).
• Reporting: define metrics and acceptance criteria; partner on dashboard requirements.

What to Expect

This is a triage environment, not a steady-state one. The function is young, the data has known gaps, and the work is to stabilize and extend, not maintain and optimize. You’ll be building the plane while flying it, alongside a small team that operates with high autonomy and a bias toward measurable outcomes. If ambiguity and mess energize you, this is the right fit.

Success Metrics (6–12 Months)

Pipeline Reliability

• Scheduled pipeline runs execute without function-lead intervention; failure-to-resolution cycle time under 24 hours for non-blocking issues.
• Agent coverage extended to new data elements as prioritized (measured by number of signals under active detection).

Detection & Resolution Quality

• Sentinel detection precision and recall improve quarter over quarter, tracked by segment.
• Concentrix resolution queue throughput and accuracy meet defined acceptance thresholds.
• False-positive rate decreases through feedback-loop refinement.

Data Quality Outcomes

• Tier-1 field fill-rates: Country ≥95%; Vertical ≥90% at ≥85% sampled accuracy; Revenue bands ≥90%.
• Hierarchy coverage 65–80%+ across target segments.
• Enterprise cost-per-record reduction of 30–40% via AI-first + selective vendor usage.

Qualifications

Required

• 3–6 years in Data Ops, Sales Ops, or GTM Ops with hands-on data quality ownership for account and contact data.
• Proficiency with Snowflake (SQL for querying, analysis, validation) and SFDC (object model, field configuration, data flows).
• Working experience with Claude Code or comparable LLM-based tooling in an operational (not just experimental) context.
• Experience designing and running AI-assisted enrichment workflows (e.g., Clay + LLM prompts) and evaluating accuracy/coverage.
• Comfort operating in a command-line environment: tmux, shell scripts, log analysis, batch process monitoring.
• Process design mindset with a bias toward measurable outcomes; strong written communication.

Strong Plus

• Experience with account/contact data vendors (D&B, ZoomInfo, Clearbit, StoreLeads) and waterfall enrichment logic.
• Python for QA scripting, sampling, or light automation.
• Familiarity with prompt engineering, confidence scoring, and AI guardrails (evidence capture, versioned prompts, QA sampling gates).

Tool Stack

• Core: Snowflake (SQL), SFDC, Claude Code, Clay
• Pipeline: Shell orchestration, Cartographer / Sentinel / Resolver agents
• Enrichment: D&B, ZoomInfo, Clearbit, StoreLeads, LLM prompts
• Nice to Have: Python, SOQL, prompt engineering frameworks
• AI Guardrails (Expected Practice): Confidence floors, evidence capture, versioned prompts, 10% QA sampling gates, audit-on-promote, drift alerts, and privacy/compliance checks. This role is expected to uphold and improve these practices, not just follow them.

Why This Role, Why Now

GTM Data Strategy & Operations stood up from scratch with no predecessor. Today the function runs on three offshore contractors and zero FTEs, managed by a single leader who is simultaneously building the agentic infrastructure, operating it in production, and driving major initiatives (hierarchy redesign, data quality assessment, vendor optimization).

The operating model is deliberately agentic AI–first: a multi-agent pipeline (Cartographer, Sentinel, Resolver, Reporting) handles detection, enrichment, hierarchy mapping, and conflict resolution at scale. This is not a future-state vision, these agents are live and processing enterprise account families in production today.

The problem: one person cannot build, operate, and extend this system while also managing strategic workstreams. The function currently covers only core Tier‑1 fields. Dozens of account, contact, and lead signals remain unaddressed. Every pipeline run, every failure diagnosis, and every offshore handoff flows through a single point of failure.

This role is the first onshore execution hire for an agent operator who can keep the system running, improve it, and extend detection and resolution coverage as GTM leadership prioritizes new data elements.

Role Summary

Sit between AI systems and GTM data. Operate, tune, and extend our agentic data quality pipeline (detection, enrichment, hierarchy mapping, conflict resolution) so it runs reliably, improves continuously, and expands to cover more of the data landscape. Own the handoff between automated output and human review, managing quality and throughput with our offshore team. You don’t build agents from scratch, but you run them, evaluate their output with GTM data judgment, and make them better.

Core Responsibilities

Agent Pipeline Operations

• Run and monitor production pipeline sessions (Cartographer, Sentinel, Resolver) across scheduled cadences; diagnose and resolve failures (API errors, session timeouts, data anomalies) without escalating to the function lead.
• Execute pipeline runs in Claude Claude and tmux; manage long-running batch processes; interpret logs and output to confirm data integrity before downstream handoff.
• Maintain pipeline orchestration scripts and configuration; extend agent coverage as new data elements are prioritized by GTM leadership.

Agent Tuning & Improvement

• Refine detection rules, prompt logic, and confidence thresholds based on output analysis and false-positive/negative patterns.
• Evaluate agent accuracy by segment (Enterprise vs. MM/SMB) and recommend rule or workflow changes backed by evidence.
• Run bake-offs (vendor vs. AI enrichment) to optimize cost, coverage, and accuracy; document results for decision-making.

Sentinel → Offshore Resolution Loop

• Own the handoff between Sentinel detection output and Concentrix triage queues; define queue structure, priority tiers, and resolution instructions.
• Monitor offshore resolution quality and throughput; refine detection rules based on patterns surfaced through triage.
• Close the feedback loop: track resolution outcomes back to agent configuration to reduce recurring false positives and improve detection precision.

Data Quality & Enrichment Operations

• Maintain ops-only staging fields; manage the promote-to-production flow with audit controls.
• Design and run AI-assisted enrichment workflows (Clay + LLM prompts) with evidence links and confidence thresholds.
• Monitor fill-rate, sampled accuracy, freshness, and cost-per-record by source and segment; surface vendor performance issues and recommend changes.
• Keep data dictionaries, SOPs, and runbooks current as agents and processes evolve.

Cross-Functional Partnership

• GTM Systems (SFDC): field configuration, permission sets, automation, flows.
• Data Engineering: source availability, ID mapping, lineage (no pipeline coding).
• Reporting: define metrics and acceptance criteria; partner on dashboard requirements.

What to Expect

This is a triage environment, not a steady-state one. The function is young, the data has known gaps, and the work is to stabilize and extend, not maintain and optimize. You’ll be building the plane while flying it, alongside a small team that operates with high autonomy and a bias toward measurable outcomes. If ambiguity and mess energize you, this is the right fit.

Success Metrics (6–12 Months)

Pipeline Reliability

• Scheduled pipeline runs execute without function-lead intervention; failure-to-resolution cycle time under 24 hours for non-blocking issues.
• Agent coverage extended to new data elements as prioritized (measured by number of signals under active detection).

Detection & Resolution Quality

• Sentinel detection precision and recall improve quarter over quarter, tracked by segment.
• Concentrix resolution queue throughput and accuracy meet defined acceptance thresholds.
• False-positive rate decreases through feedback-loop refinement.

Data Quality Outcomes

• Tier-1 field fill-rates: Country ≥95%; Vertical ≥90% at ≥85% sampled accuracy; Revenue bands ≥90%.
• Hierarchy coverage 65–80%+ across target segments.
• Enterprise cost-per-record reduction of 30–40% via AI-first + selective vendor usage.

Qualifications

Required

• 3–6 years in Data Ops, Sales Ops, or GTM Ops with hands-on data quality ownership for account and contact data.
• Proficiency with Snowflake (SQL for querying, analysis, validation) and SFDC (object model, field configuration, data flows).
• Working experience with Claude Code or comparable LLM-based tooling in an operational (not just experimental) context.
• Experience designing and running AI-assisted enrichment workflows (e.g., Clay + LLM prompts) and evaluating accuracy/coverage.
• Comfort operating in a command-line environment: tmux, shell scripts, log analysis, batch process monitoring.
• Process design mindset with a bias toward measurable outcomes; strong written communication.

Strong Plus

• Experience with account/contact data vendors (D&B, ZoomInfo, Clearbit, StoreLeads) and waterfall enrichment logic.
• Python for QA scripting, sampling, or light automation.
• Familiarity with prompt engineering, confidence scoring, and AI guardrails (evidence capture, versioned prompts, QA sampling gates).

Tool Stack

• Core: Snowflake (SQL), SFDC, Claude Code, Clay
• Pipeline: Shell orchestration, Cartographer / Sentinel / Resolver agents
• Enrichment: D&B, ZoomInfo, Clearbit, StoreLeads, LLM prompts
• Nice to Have: Python, SOQL, prompt engineering frameworks
• AI Guardrails (Expected Practice): Confidence floors, evidence capture, versioned prompts, 10% QA sampling gates, audit-on-promote, drift alerts, and privacy/compliance checks. This role is expected to uphold and improve these practices, not just follow them.

Why This Role, Why Now

GTM Data Strategy & Operations stood up from scratch with no predecessor. Today the function runs on three offshore contractors and zero FTEs, managed by a single leader who is simultaneously building the agentic infrastructure, operating it in production, and driving major initiatives (hierarchy redesign, data quality assessment, vendor optimization).

The operating model is deliberately agentic AI–first: a multi-agent pipeline (Cartographer, Sentinel, Resolver, Reporting) handles detection, enrichment, hierarchy mapping, and conflict resolution at scale. This is not a future-state vision, these agents are live and processing enterprise account families in production today.

The problem: one person cannot build, operate, and extend this system while also managing strategic workstreams. The function currently covers only core Tier‑1 fields. Dozens of account, contact, and lead signals remain unaddressed. Every pipeline run, every failure diagnosis, and every offshore handoff flows through a single point of failure.

This role is the first onshore execution hire for an agent operator who can keep the system running, improve it, and extend detection and resolution coverage as GTM leadership prioritizes new data elements.

Role Summary

Sit between AI systems and GTM data. Operate, tune, and extend our agentic data quality pipeline (detection, enrichment, hierarchy mapping, conflict resolution) so it runs reliably, improves continuously, and expands to cover more of the data landscape. Own the handoff between automated output and human review, managing quality and throughput with our offshore team. You don’t build agents from scratch, but you run them, evaluate their output with GTM data judgment, and make them better.

Core Responsibilities

Agent Pipeline Operations

• Run and monitor production pipeline sessions (Cartographer, Sentinel, Resolver) across scheduled cadences; diagnose and resolve failures (API errors, session timeouts, data anomalies) without escalating to the function lead.
• Execute pipeline runs in Claude Claude and tmux; manage long-running batch processes; interpret logs and output to confirm data integrity before downstream handoff.
• Maintain pipeline orchestration scripts and configuration; extend agent coverage as new data elements are prioritized by GTM leadership.

Agent Tuning & Improvement

• Refine detection rules, prompt logic, and confidence thresholds based on output analysis and false-positive/negative patterns.
• Evaluate agent accuracy by segment (Enterprise vs. MM/SMB) and recommend rule or workflow changes backed by evidence.
• Run bake-offs (vendor vs. AI enrichment) to optimize cost, coverage, and accuracy; document results for decision-making.

Sentinel → Offshore Resolution Loop

• Own the handoff between Sentinel detection output and Concentrix triage queues; define queue structure, priority tiers, and resolution instructions.
• Monitor offshore resolution quality and throughput; refine detection rules based on patterns surfaced through triage.
• Close the feedback loop: track resolution outcomes back to agent configuration to reduce recurring false positives and improve detection precision.

Data Quality & Enrichment Operations

• Maintain ops-only staging fields; manage the promote-to-production flow with audit controls.
• Design and run AI-assisted enrichment workflows (Clay + LLM prompts) with evidence links and confidence thresholds.
• Monitor fill-rate, sampled accuracy, freshness, and cost-per-record by source and segment; surface vendor performance issues and recommend changes.
• Keep data dictionaries, SOPs, and runbooks current as agents and processes evolve.

Cross-Functional Partnership

• GTM Systems (SFDC): field configuration, permission sets, automation, flows.
• Data Engineering: source availability, ID mapping, lineage (no pipeline coding).
• Reporting: define metrics and acceptance criteria; partner on dashboard requirements.

What to Expect

This is a triage environment, not a steady-state one. The function is young, the data has known gaps, and the work is to stabilize and extend, not maintain and optimize. You’ll be building the plane while flying it, alongside a small team that operates with high autonomy and a bias toward measurable outcomes. If ambiguity and mess energize you, this is the right fit.

Success Metrics (6–12 Months)

Pipeline Reliability

• Scheduled pipeline runs execute without function-lead intervention; failure-to-resolution cycle time under 24 hours for non-blocking issues.
• Agent coverage extended to new data elements as prioritized (measured by number of signals under active detection).

Detection & Resolution Quality

• Sentinel detection precision and recall improve quarter over quarter, tracked by segment.
• Concentrix resolution queue throughput and accuracy meet defined acceptance thresholds.
• False-positive rate decreases through feedback-loop refinement.

Data Quality Outcomes

• Tier-1 field fill-rates: Country ≥95%; Vertical ≥90% at ≥85% sampled accuracy; Revenue bands ≥90%.
• Hierarchy coverage 65–80%+ across target segments.
• Enterprise cost-per-record reduction of 30–40% via AI-first + selective vendor usage.

Qualifications

Required

• 3–6 years in Data Ops, Sales Ops, or GTM Ops with hands-on data quality ownership for account and contact data.
• Proficiency with Snowflake (SQL for querying, analysis, validation) and SFDC (object model, field configuration, data flows).
• Working experience with Claude Code or comparable LLM-based tooling in an operational (not just experimental) context.
• Experience designing and running AI-assisted enrichment workflows (e.g., Clay + LLM prompts) and evaluating accuracy/coverage.
• Comfort operating in a command-line environment: tmux, shell scripts, log analysis, batch process monitoring.
• Process design mindset with a bias toward measurable outcomes; strong written communication.

Strong Plus

• Experience with account/contact data vendors (D&B, ZoomInfo, Clearbit, StoreLeads) and waterfall enrichment logic.
• Python for QA scripting, sampling, or light automation.
• Familiarity with prompt engineering, confidence scoring, and AI guardrails (evidence capture, versioned prompts, QA sampling gates).

Tool Stack

• Core: Snowflake (SQL), SFDC, Claude Code, Clay
• Pipeline: Shell orchestration, Cartographer / Sentinel / Resolver agents
• Enrichment: D&B, ZoomInfo, Clearbit, StoreLeads, LLM prompts
• Nice to Have: Python, SOQL, prompt engineering frameworks
• AI Guardrails (Expected Practice): Confidence floors, evidence capture, versioned prompts, 10% QA sampling gates, audit-on-promote, drift alerts, and privacy/compliance checks. This role is expected to uphold and improve these practices, not just follow them.

Summary

Klaviyo is looking for a Lead Data Science Analyst to join our GTM Strategic Analytics & Insights team. In this role, you will serve as a senior individual contributor at the intersection of advanced data science, AI/LLM-driven innovation, and Go-to-Market strategy. You will build and maintain sophisticated predictive and inferential models, conduct deep-dive statistical analyses, and develop AI-first solutions that unlock meaningful insights across the pre and post Sales Customer lifecycle.

The successful candidate will partner closely with GTM leadership to shape how Klaviyo understands, measures, and accelerates new business and customer outcomes; from pre-sales motion through onboarding, expansion and retention. You will operate with a strong bias toward AI-augmented workflows and bring a modern, LLM-aware approach to every analytical challenge.

The ideal candidate is intellectually curious, strategically minded, and energized by hard problems. They bring deep technical fluency across the full data science stack, a demonstrated ability to influence senior stakeholders through clear storytelling, and a genuine commitment to building AI-first solutions in a fast-moving SaaS environment.

How You Will Make a Difference

• Build and maintain advanced models: Build and maintain advanced predictive and time-series models: design, train, deploy, and monitor models across use cases such as demand forecasting, capacity planning, deal scoring, and customer propensity; incorporate seasonality, exogenous drivers, and backtesting frameworks to ensure accuracy and robustness
• Apply statistical rigor: lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and other statistical methods to surface actionable signals from complex, large-scale datasets
• Develop AI/LLM-powered solutions: architect and implement AI-first analyses and tooling using large language models, prompt engineering, retrieval-augmented generation (RAG), and related techniques to automate insight generation, surface qualitative signals at scale, and augment team capabilities
• Own forecasting and decision systems: Own end-to-end forecasting and operational decision systems, including time-series demand forecasting, capacity planning models (e.g., Erlang-based staffing), and production pipelines that power GTM and Support planning workflows; ensure reliability, scalability, and business adoption of outputs
• Drive customer intelligence: develop and maintain prospect, deal health, archetype, and capacity models that inform GTM strategy, planning, and growth initiatives
• Define the measurement framework: identify, create, and steward benchmarks and metrics that meaningfully represent growth, engagement, and success outcomes
• Communicate with impact: distill complex analyses into clear, cohesive narratives with executive-ready materials that drive decisions at the senior leadership level
• Collaborate cross-functionally: partner with Systems & Engineering, GTM Operations, Rev Ops & Planning, Product, Business Intelligence, Data Science, and Finance to ensure analytical solutions are integrated, scalable, and trusted

Who You Are

• 6+ years of professional experience in an advanced analytics or data science role; SaaS experience strongly preferred
• Deep expertise in statistical inference and modeling, including supervised techniques (regression, classification, gradient boosting, decision trees) and unsupervised techniques (clustering, PCA, anomaly detection, topic modeling)
• Hands-on experience designing and deploying AI/LLM-based solutions, including prompt engineering, fine-tuning, RAG pipelines, or LLM-integrated analytics workflows; you approach new problems with an AI-first mindset
• Familiarity and experience with distributed coding projects, including using Git for code management.
• Advanced proficiency in Python (pandas, numpy, scikit-learn, xgboost, statsmodels, and LLM/AI libraries such as LangChain, OpenAI SDK, or HuggingFace) and SQL; working knowledge of DBT
• Own and scale end-to-end data pipelines, including orchestration with Airflow and transformation/modeling with dbt; design reliable, testable, and modular workflows that support production-grade analytics and machine learning use cases, with a focus on performance, data quality, and maintainability.
• Develop and iterate on time-series forecasting frameworks using approaches such as ARIMA/SARIMAX, ETS, MSTL, and machine learning-based models; evaluate performance through rigorous backtesting and continuously improve model accuracy and business applicability
• Experience building data visualizations and dashboards across platforms such as Tableau, ThoughtSpot, matplotlib, seaborn, plotly, or similar tooling.
• Strong project ownership: experienced operating to a roadmap, managing milestones and deliverables, and delivering high-quality work product in a timely manner
• Comfortable with autonomy and ambiguity, with a proactive orientation toward identifying and solving problems before they're fully defined
• Excellent written and verbal communication skills, including experience preparing materials for executive audiences

Summary

Klaviyo is looking for a Lead Data Science Analyst to join our GTM Strategic Analytics & Insights team. In this role, you will serve as a senior individual contributor at the intersection of advanced data science, AI/LLM-driven innovation, and Go-to-Market strategy. You will build and maintain sophisticated predictive and inferential models, conduct deep-dive statistical analyses, and develop AI-first solutions that unlock meaningful insights across the pre and post Sales Customer lifecycle.

The successful candidate will partner closely with GTM leadership to shape how Klaviyo understands, measures, and accelerates new business and customer outcomes; from pre-sales motion through onboarding, expansion and retention. You will operate with a strong bias toward AI-augmented workflows and bring a modern, LLM-aware approach to every analytical challenge.

The ideal candidate is intellectually curious, strategically minded, and energized by hard problems. They bring deep technical fluency across the full data science stack, a demonstrated ability to influence senior stakeholders through clear storytelling, and a genuine commitment to building AI-first solutions in a fast-moving SaaS environment.

How You Will Make a Difference

• Build and maintain advanced models: Build and maintain advanced predictive and time-series models: design, train, deploy, and monitor models across use cases such as demand forecasting, capacity planning, deal scoring, and customer propensity; incorporate seasonality, exogenous drivers, and backtesting frameworks to ensure accuracy and robustness
• Apply statistical rigor: lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and other statistical methods to surface actionable signals from complex, large-scale datasets
• Develop AI/LLM-powered solutions: architect and implement AI-first analyses and tooling using large language models, prompt engineering, retrieval-augmented generation (RAG), and related techniques to automate insight generation, surface qualitative signals at scale, and augment team capabilities
• Own forecasting and decision systems: Own end-to-end forecasting and operational decision systems, including time-series demand forecasting, capacity planning models (e.g., Erlang-based staffing), and production pipelines that power GTM and Support planning workflows; ensure reliability, scalability, and business adoption of outputs
• Drive customer intelligence: develop and maintain prospect, deal health, archetype, and capacity models that inform GTM strategy, planning, and growth initiatives
• Define the measurement framework: identify, create, and steward benchmarks and metrics that meaningfully represent growth, engagement, and success outcomes
• Communicate with impact: distill complex analyses into clear, cohesive narratives with executive-ready materials that drive decisions at the senior leadership level
• Collaborate cross-functionally: partner with Systems & Engineering, GTM Operations, Rev Ops & Planning, Product, Business Intelligence, Data Science, and Finance to ensure analytical solutions are integrated, scalable, and trusted

Who You Are

• 6+ years of professional experience in an advanced analytics or data science role; SaaS experience strongly preferred
• Deep expertise in statistical inference and modeling, including supervised techniques (regression, classification, gradient boosting, decision trees) and unsupervised techniques (clustering, PCA, anomaly detection, topic modeling)
• Hands-on experience designing and deploying AI/LLM-based solutions, including prompt engineering, fine-tuning, RAG pipelines, or LLM-integrated analytics workflows; you approach new problems with an AI-first mindset
• Familiarity and experience with distributed coding projects, including using Git for code management.
• Advanced proficiency in Python (pandas, numpy, scikit-learn, xgboost, statsmodels, and LLM/AI libraries such as LangChain, OpenAI SDK, or HuggingFace) and SQL; working knowledge of DBT
• Own and scale end-to-end data pipelines, including orchestration with Airflow and transformation/modeling with dbt; design reliable, testable, and modular workflows that support production-grade analytics and machine learning use cases, with a focus on performance, data quality, and maintainability.
• Develop and iterate on time-series forecasting frameworks using approaches such as ARIMA/SARIMAX, ETS, MSTL, and machine learning-based models; evaluate performance through rigorous backtesting and continuously improve model accuracy and business applicability
• Experience building data visualizations and dashboards across platforms such as Tableau, ThoughtSpot, matplotlib, seaborn, plotly, or similar tooling.
• Strong project ownership: experienced operating to a roadmap, managing milestones and deliverables, and delivering high-quality work product in a timely manner
• Comfortable with autonomy and ambiguity, with a proactive orientation toward identifying and solving problems before they're fully defined
• Excellent written and verbal communication skills, including experience preparing materials for executive audiences

About the Role

Klaviyo is seeking a Senior Manager, Partner Operations to help drive the evolution of our global partner ecosystem—ensuring that our services, technology, and strategic alliances deliver measurable value to Klaviyo, our partners, and our customers. In this highly cross-functional role, you’ll serve as a key connector between strategy and execution—owning the operational infrastructure, analytics, and processes that support our agency, tech, and product partnerships.

You’ll work hand-in-hand with partner leadership, marketing, sales, product, enablement, and GTM operations to help scale partner programs, define KPIs, streamline processes, and ensure operational excellence at every stage of the partner lifecycle. This role requires a strong mix of strategic thinking, data analysis, systems expertise, and hands-on execution.

How You’ll Make an Impact

Operational Excellence & Systems

• Design and own scalable partner operations infrastructure, including onboarding, training, support, and co-selling workflows.
• Collaborate with CS Ops, Sales Ops, and Marketing Ops to integrate partner motions into our core GTM systems and reporting.
• Develop and manage partner tiering frameworks, benefits, and incentive models to support program scalability and partner satisfaction.
• Implement mechanisms to track partner program OKRs and support monthly/quarterly business reviews (MBRs/QBRs) with clear, data-driven reporting.
• Transform workflows by putting AI at the center, building smarter systems and ways of working from the ground up.

Insights, Analytics & Planning

• Define and monitor key partner performance metrics, pipeline activity, and revenue contribution—turning insights into recommendations.
• Lead annual planning process for partnerships team–including headcount and quota planning, territory design and compensation planning
• Stay informed on industry trends, competitive dynamics, and best practices to ensure Klaviyo’s partner ecosystem remains best-in-class.

Cross-Functional Collaboration

• Act as the connective tissue between Partnerships, Sales, Marketing, Customer Success, Product, and GTM Operatons—aligning priorities and facilitating seamless program execution.
• Support strategic initiatives such as new market entry, vertical expansion, and partner-led co-marketing campaigns.
• Partner with Enablement to ensure partners are trained and positioned to deliver value across the customer journey.

Leadership & Influence

• Mentor junior team members and serve as a thought partner to partner program leaders.
• Effectively communicate strategies, insights, and recommendations to stakeholders at all levels—including executive leadership.
• Bring a “builder” mindset to partner operations: constantly seeking opportunities to improve and scale what works.

Who You Are

• 8–10 years of experience in partner/channel operations, GTM strategy, revenue operations, or management consulting—ideally in a high-growth B2B SaaS environment.
• 3+ years of experience working directly with partner programs (agency, tech, or strategic alliances).
• Proven ability to manage complex projects, influence cross-functional stakeholders, and balance strategic thinking with hands-on execution.
• Strong understanding of Salesforce, PRM platforms, and BI/reporting tools.
• Analytical and data-driven: you turn data into actionable insights and operational improvements.
• Exceptional written and verbal communication skills with experience engaging senior and executive-level stakeholders.
• Comfortable navigating ambiguity, shifting priorities, and working independently in a fast-paced environment.
• Bachelor’s degree required; MBA or equivalent experience preferred.
• You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient.

#LI-Hybrid

#LI-BAILEY

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

Role Summary

Klaviyo is seeking a Manager of Advanced Analytics & Automation to lead strategic analytics programs and AI first automations for our Customer Success Strategy and Analytics team. This role owns the development of scalable customer intelligence systems, and spans customer cohorting, health and success signals, churn and expansion drivers, and AI-powered workflows that translate insight into action.

You will operate as a senior technical individual contributor with program-level ownership, combining advanced data analytics and applied AI in partnership to build durable tools and processes used across GTM. This role is ideal for someone who can architect, build, QA, and operationalize advanced analytics and insights projects end-to-end while partnering deeply across Product, Ops, and Data teams.

What You’ll Do

• Own Customer Success Intelligence & Advanced Analytics & Insights, design Production-Grade Data Models & Analytics Pipelines, and champion AI Tooling, Automation & Workflow Orchestration
• Build and maintain customer cohorting, segmentation, and benchmarking frameworks, in collaboration with our Product Data Science team, that captures meaningful differences in Customer behavior, maturity, and value
• Define and evolve customer health, adoption, and success scoring and signals, including leading indicators of churn and expansion
• Design and scale advanced analytics projects and models, including cohort analysis, statistical inference, and impact measurement systems, that connect product usage and engagement to customer retention outcomes
• Act as a technical program owner for complex analytics initiatives, driving roadmap definition, sequencing workstreams, coordinating dependencies, and delivering outcomes
• Create advanced SQL and Python outputs that power customer insights, forecasting inputs, and operational reporting, using GitHub for version control and change management, and applying strong data modeling and governance practices
• Partner closely with Data Engineering, CS OPs, Data Science and Product Analytics, to deliver strategic analytical tools and insights that inform GTM priorities, roadmap decisions, and customer strategy, and collaborate to deliver prototype AI-assisted tools that provide GTM teams real time actionable insights into customer behavior
• Design and deploy workflow automations using n8n, Airflow or other AI tools, and establish integrated AI driven alerts that surface real-time customer risk and opportunity
• Lead initiatives end-to-end: problem framing, design, build, QA, documentation, rollout, and iteration
• Maintain a high bar for execution quality, accuracy, reliability, and long-term scalability
• Translate complex technical outputs into clear, actionable narratives for senior business and GTM stakeholders

What We’re Looking For

• 5+ years of experience in advanced analytics, technical analytics or data analytics (SaaS strongly preferred)
• Advanced proficiency in SQL and GitHub, with experience owning complex analytical data models
• Strong hands-on experience with Python and/or R, including statistical inference, cohort analysis, and advanced research
• Tableau Developer–level fluency, with solid understanding of dashboard logic, metrics, and database structures
• Confident using AI tooling (e.g., Cursor, Glean, Codex, GitHub AI etc.) to accelerate development, and familiarity with workflow automation platforms (n8n, Airflow, DBT); experience with LangChain a plus
• Demonstrated ability to build and operationalize analytics and AI workflows used by CS, Sales, or GTM teams
• Experience operating as a technical program manager for analytics or data initiatives
• Strong communication skills and comfort partnering with senior, non-technical stakeholders
• High standards for analytical rigor, QA, documentation, and system reliability
• You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient. 

#LI-Abbey

#LI-hybrid

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

About the Role

We’re looking for a systems-minded and customer-obsessed Senior Manager of Customer Success Operations to accelerate Klaviyo’s Digital-first and At Scale engagement model. In this role, you’ll partner closely with the Digital and At Scale Customer Success teams, as well as work with Product, Customer Education and Marketing to operationalize the tools, data, automation, and processes that help customers learn, adopt, and grow at scale.

Working in close partnership with the GTM systems and Customer Success teams, you’ll drive the operational engine behind digital and hybrid engagement models including lifecycle automation, AI-powered engagement, adoption/education enablement. If you thrive at the intersection of technology, data, and customer experience—and enjoy turning strategy into scalable, measurable outcomes—this role gives you the opportunity to shape how tens of thousands of customers experience Klaviyo.

How You’ll Make an Impact

• Translate strategic digital CS goals into execution plans—including system changes, cross-functional alignment, and project management
• Help design and execute scalable lifecycle workflows, triggers, and automations that power onboarding, adoption, expansion, and ongoing customer success
• Operationalize digital customer lifecycle programs including triggered communications, in-app engagements, personalized guidance, and automated success plans
• Optimize the digital CS tech stack—including Gainsight CS/PX, automation tools, AI agents, LMS platforms, and community systems
• Lead change management efforts tied to digital transformation initiatives within the broader Customer Success organization
• Deploy AI-driven insights, nudges, and in-product guidance to accelerate customer engagement and reduce manual CS effort
• Collaborate with Community and Education teams to operationalize learning journeys, certifications, and peer-driven engagement
• Maintain operational rigor through standardized processes, documentation, governance, and change management

What You Bring

• 7+ years of experience in Customer Success Operations, Digital CS, or scaled CS programs within a B2B SaaS environment
• Hands-on expertise with Gainsight CS/PX and modern customer lifecycle automation tools; familiarity with LMS, community, and support platforms
• Experience designing automated journeys, workflows, and digital engagement programs
• Comfort working with AI-driven tooling (agents, copilots, rule logic) in customer-facing contexts
• Strong analytical skills, with experience building dashboards, KPIs, and insights using multi-source data
• Ability to influence cross-functional partners and translate strategy into clear operational plans
• Clear communication, strong project management, and a bias toward action and continuous improvement
• Bachelor’s degree in Business, Operations, Information Systems, Analytics, or related field (or equivalent experience)
• You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient. 

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

#LI-HYRBID

#LI-ABBEY

Role Summary

Klaviyo is seeking a Manager of Advanced Analytics & Automation to lead strategic analytics programs and AI first automations for our Customer Success Strategy and Analytics team. This role owns the development of scalable customer intelligence systems, and spans customer cohorting, health and success signals, churn and expansion drivers, and AI-powered workflows that translate insight into action.

You will operate as a senior technical individual contributor with program-level ownership, combining advanced data analytics and applied AI in partnership to build durable tools and processes used across GTM. This role is ideal for someone who can architect, build, QA, and operationalize advanced analytics and insights projects end-to-end while partnering deeply across Product, Ops, and Data teams.

What You’ll Do

• Own Customer Success Intelligence & Advanced Analytics & Insights, design Production-Grade Data Models & Analytics Pipelines, and champion AI Tooling, Automation & Workflow Orchestration
• Build and maintain customer cohorting, segmentation, and benchmarking frameworks, in collaboration with our Product Data Science team, that captures meaningful differences in Customer behavior, maturity, and value
• Define and evolve customer health, adoption, and success scoring and signals, including leading indicators of churn and expansion
• Design and scale advanced analytics projects and models, including cohort analysis, statistical inference, and impact measurement systems, that connect product usage and engagement to customer retention outcomes
• Act as a technical program owner for complex analytics initiatives, driving roadmap definition, sequencing workstreams, coordinating dependencies, and delivering outcomes
• Create advanced SQL and Python outputs that power customer insights, forecasting inputs, and operational reporting, using GitHub for version control and change management, and applying strong data modeling and governance practices
• Partner closely with Data Engineering, CS OPs, Data Science and Product Analytics, to deliver strategic analytical tools and insights that inform GTM priorities, roadmap decisions, and customer strategy, and collaborate to deliver prototype AI-assisted tools that provide GTM teams real time actionable insights into customer behavior
• Design and deploy workflow automations using n8n, Airflow or other AI tools, and establish integrated AI driven alerts that surface real-time customer risk and opportunity
• Lead initiatives end-to-end: problem framing, design, build, QA, documentation, rollout, and iteration
• Maintain a high bar for execution quality, accuracy, reliability, and long-term scalability
• Translate complex technical outputs into clear, actionable narratives for senior business and GTM stakeholders

What We’re Looking For

• 5+ years of experience in advanced analytics, technical analytics or data analytics (SaaS strongly preferred)
• Advanced proficiency in SQL and GitHub, with experience owning complex analytical data models
• Strong hands-on experience with Python and/or R, including statistical inference, cohort analysis, and advanced research
• Tableau Developer–level fluency, with solid understanding of dashboard logic, metrics, and database structures
• Confident using AI tooling (e.g., Cursor, Glean, Codex, GitHub AI etc.) to accelerate development, and familiarity with workflow automation platforms (n8n, Airflow, DBT); experience with LangChain a plus
• Demonstrated ability to build and operationalize analytics and AI workflows used by CS, Sales, or GTM teams
• Experience operating as a technical program manager for analytics or data initiatives
• Strong communication skills and comfort partnering with senior, non-technical stakeholders
• High standards for analytical rigor, QA, documentation, and system reliability
• You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient. 

#LI-Abbey

#LI-hybrid

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here