Lead Data Analyst

Felix Technologies

2d•Remote

About The Position

We are looking for a highly analytical and business-oriented Lead Data Analyst to lead a multidisciplinary team across Chat, Retention, AI, and Operations. This role sits at the intersection of Data, Product, and Customer Experience. The ideal candidate will be able to deeply understand the business, map the key drivers and KPIs across the customer journey, identify opportunities and friction points, and translate data into actions that improve conversion, retention, and operational efficiency. This role is 40% hands on and 60% working closely with stakeholders and guiding the team. Beyond managing the team, this person will be expected to raise the analytical bar of the organization —bringing structure to complex problems, moving quickly from ambiguous questions to actionable insights, and partnering closely with stakeholders to drive measurable business impact.

Requirements

6+ years of experience in Data Science, Analytics, Product Analytics, or a related field.
Strong analytical and problem-solving skills, with the ability to break down complex and ambiguous problems.
Strong SQL skills and experience working with large datasets.
Strong understanding of product metrics, funnels, cohorts, retention, experimentation, and behavioral analytics.
Ability to move beyond reporting and understand why metrics move and what actions can influence them.
Strong business acumen and curiosity—you enjoy getting deep into how the business actually works.
Experience managing and developing analytical teams.
Exceptional stakeholder management and communication skills.
Ability to work effectively with very different audiences, from technical teams to Product, Operations, and senior leadership.
Ability to influence decisions without relying solely on formal authority.
Comfortable challenging stakeholders and translating complex analyses into simple, actionable narratives.
Strong SQL and data analysis capabilities.
Experience with product analytics tools such as Amplitude, Mixpanel, or similar.
Experience with BI and visualization tools (we use Omni).
Familiarity with experimentation and causal inference is a strong plus.
Experience with AI/LLM products or conversational analytics is a plus.
Experience in fintech, conversational products, or other high-growth environments is a plus.

Nice To Haves

Familiarity with experimentation and causal inference is a strong plus.
Experience with AI/LLM products or conversational analytics is a plus.
Experience in fintech, conversational products, or other high-growth environments is a plus.

Responsibilities

Lead the analytics agenda for the Chat experience, covering both upper and lower funnel and different geographies.
Partner closely with Product stakeholders to understand and improve the conversion funnel.
Build and evolve dashboards and analytical frameworks, primarily using Amplitude, to monitor funnel performance and identify opportunities.
Identify key conversion drivers, drop-offs, and friction points across the customer journey.
Turn behavioral data into clear product recommendations and experiments.
Lead the analytical strategy around user retention and lifecycle behavior.
Develop a deep understanding of the different friction points users experience throughout their journey.
Analyze changes in user composition, behavior, and lifecycle mix to understand what is driving retention changes.
Build frameworks to identify the root causes behind retention increases or declines.
Partner with Product, Operations, Growth and Fintech teams to prioritize initiatives based on their expected impact.
Develop a data-driven understanding of how users interact with AI-powered experiences.
Identify where and why conversational experiences break down and how those failures impact conversion and retention.
Define and monitor key metrics across the conversational funnel.
Partner with AI/Product teams to identify opportunities to improve the experience through data and experimentation.
Build a strong understanding of operator queues, workload, performance, and operational efficiency.
Develop scalable analytics and tools to monitor operator performance and identify bottlenecks.
Partner with Customer Experience to improve how operators handle conversations by providing them with better context and actionable information before and during interactions.
Explore and experiment with operators as a lever for retention and customer experience, measuring their incremental impact through rigorous experimentation.
Help transition operational analytics from reactive reporting to proactive decision-making.
Lead and develop a team of highly analytical data professionals.
Establish a clear analytical roadmap aligned with company and product priorities.
Deeply understand the business and build a comprehensive map of KPIs, drivers, and relationships across the customer lifecycle.
Quickly investigate ambiguous business problems and turn them into structured analytical questions.
Raise the quality, speed, and impact of analytical work across the team.
Ensure that insights are not just descriptive but lead to clear actions, experiments, and measurable outcomes.
Build strong relationships with a broad range of stakeholders across Product, Operations, AI, and Leadership.
Challenge assumptions and bring a data-driven perspective to strategic decisions.
Create scalable frameworks, dashboards, and tools that enable stakeholders to answer questions independently.
Identify opportunities where data, automation, or AI can significantly improve how the organization operates.