Mercury is at a similar moment to when railroads expanded their networks, transforming commerce. With over 300,000 businesses relying on Mercury to run their finances, we're looking for an early member of our Account Development team to help customers move from initial adoption to deeper, more connected usage.

As an Account Development Manager, you'll work directly with Mercury customers at the moments that matter most in their journey, providing a human touch when timing and context are everything. This is a quota-carrying role focused on expansion and adoption, while also serving as a critical feedback loop to inform how Mercury continues to evolve.

Responsibilities:

• Identify high-propensity accounts by leveraging product-usage data, lifecycle triggers, and predictive scoring models,engaging where additional solutions (Credit, Treasury, Working Capital, etc.) will create the most value.
• Consistently exceed monthly expansion goals by building and maintaining a robust pipeline.
• Design smart outreach, combining data insights, account research, and discovery to craft scalable one-to-many campaigns and targeted, high-touch engagements for top-value opportunities.
• Partner with Product and Marketing to test, learn, and refine our evolving go-to-market playbook.
• Master, and help evolve, our internal tools and processes to manage a high-velocity pipeline with maximum efficiency.
• Champion the Voice of the Customer, channeling feedback to Product and cross-functional teams to influence roadmap and strategy.

Requirements:

• 2–4 years in a quota-carrying Growth Sales, Account Management, or Customer Expansion role (FinTech a plus).
• Compelling communicator: able to translate complex concepts into crisp value propositions for founders, finance teams, and operators.
• Expansion Performance: Proven track record exceeding upsell targets through data-driven prioritization in a high-velocity environment.
• Builder’s mindset: energized by creating and iterating on processes within a fast-growing team.
• Bias to action & autonomy: swiftly converts new product launches and insights into cross-functional plays that reach customers and drive growth.
• Passion for FinTech or banking: you’re excited about shaping the future of modern financial services.

Total Rewards Package: The total rewards package at Mercury includes base salary, equity (stock options), and benefits. Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.

Target New Hire Base Salary Ranges:

• US employees in New York City, Los Angeles, Seattle, or the San Francisco Bay Area: $119,400-$134,300
• US employees outside of New York City, Los Angeles, Seattle, or the San Francisco Bay Area: $107,500-$120,900

Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic.

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This position requires strong expertise in commercial credit risk, advanced analytical skills, and the ability to translate complex insights into actionable strategies.

Job Responsibilities:

• Lead the development of best in class data-driven credit policies and business strategies: Leverage external (e.g. banking, tax, financial, credit bureau and other data) and internal performance data to develop credit policies (e.g. underwriting criteria, dynamic credit limit programs, etc.). Minimize credit loss by developing and implementing appropriate processes and procedures to identify and mitigate high risk customers. Design and implement data-driven strategies for customer management, including policies for credit line increases, decreases, and line reinstatements. Work closely with the sales and business development teams to support business growth strategies that preserve effective underwriting and ensure the appropriate application of Jeeves’ credit policy.

• Deliver advancements in data and analytics: Contribute to the development of a data and analytics framework to improve processing, underwriting, tracking, risk management, and reporting procedures. Design and execute data driven analyses and tracking procedures to enhance insights on credit risk for individual, and the portfolio of companies. Leverage predictive statistical methodologies (e.g., linear/logistic regression, segmentation analysis) to draw insights and develop business strategies. Partner with data scientists to build and leverage credit risk models to optimize credit policies and improve business performance.

• Improve credit monitoring and governance: Define key performance indicators (KPIs) for credit portfolios; build dashboards to monitor portfolio health and strategy performance. Develop and implement improvements to credit portfolio monitoring, client review tracking, management reporting, and customer risk rating assignments. Partner with product, engineering, and finance teams to create and maintain risk adjusted returns framework, including loss forecasting, and execute credit policies and features Implement risk metric monitoring and develop risk mitigation strategies.

• Provide guidance and mentorship to junior analysts on the team, fostering a culture of rigorous analysis and innovation.

Required Qualifications:

• 4+ years in analytics within credit risk management.
• Fluent in English.
• Excellent written and verbal communication skills.
• Intellectual curiosity.
• Experience in commercial credit risk, specifically within credit cards, payments, lending, or related industries.
• Proven ability to translate complex data and model results into actionable business strategies.
• Advanced proficiency in SQL and Excel.
• Experience using Python or R for data analysis and statistical modeling (regression, clustering, etc.).

Preferred Qualifications:

• Experience in architecting, implementing, and interpreting risk/scoring models in conjunction with data science teams.
• Experience in high growth startups.
• Experience with visualization tools such as Tableau.
• Experience building complex financial products and models.

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This position requires strong expertise in commercial credit risk, advanced analytical skills, and the ability to translate complex insights into actionable strategies.

Responsibilities:

• Lead the development of best in class data-driven credit policies and business strategies: Leverage external (e.g. banking, tax, financial, credit bureau and other data) and internal performance data to develop credit policies (e.g. underwriting criteria, dynamic credit limit programs, etc.). Minimize credit loss by developing and implementing appropriate processes and procedures to identify and mitigate high risk customers. Design and implement data-driven strategies for customer management, including policies for credit line increases, decreases, and line reinstatements. Work closely with the sales and business development teams to support business growth strategies that preserve effective underwriting and ensure the appropriate application of Jeeves’ credit policy.

• Deliver advancements in data and analytics: Contribute to the development of a data and analytics framework to improve processing, underwriting, tracking, risk management, and reporting procedures. Design and execute data driven analyses and tracking procedures to enhance insights on credit risk for individual, and the portfolio of companies. Leverage predictive statistical methodologies (e.g., linear/logistic regression, segmentation analysis) to draw insights and develop business strategies. Partner with data scientists to build and leverage credit risk models to optimize credit policies and improve business performance.

• Improve credit monitoring and governance: Define key performance indicators (KPIs) for credit portfolios; build dashboards to monitor portfolio health and strategy performance. Develop and implement improvements to credit portfolio monitoring, client review tracking, management reporting, and customer risk rating assignments. Partner with product, engineering, and finance teams to create and maintain risk adjusted returns framework, including loss forecasting, and execute credit policies and features Implement risk metric monitoring and develop risk mitigation strategies.

• Provide guidance and mentorship to junior analysts on the team, fostering a culture of rigorous analysis and innovation.

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Key responsibilities include:

• Leading the development of best-in-class data-driven credit policies and business strategies
• Delivering advancements in data and analytics
• Improving credit monitoring and governance

This position requires strong expertise in commercial credit risk, advanced analytical skills, and the ability to translate complex insights into actionable strategies.

You will work closely with the sales and business development teams to support business growth strategies that preserve effective underwriting and ensure the appropriate application of Jeeves' credit policy.

You will also contribute to the development of a data and analytics framework to improve processing, underwriting, tracking, risk management, and reporting procedures.

In addition, you will define key performance indicators (KPIs) for credit portfolios; build dashboards to monitor portfolio health and strategy performance.

You will partner with data scientists to build and leverage credit risk models to optimize credit policies and improve business performance.

You will provide guidance and mentorship to junior analysts on the team, fostering a culture of rigorous analysis and innovation.

Required qualifications include:

• 4+ years in analytics within credit risk management
• Fluent in English
• Excellent written and verbal communication skills
• Intellectual curiosity
• Experience in commercial credit risk, specifically within credit cards, payments, lending, or related industries
• Proven ability to translate complex data and model results into actionable business strategies
• Advanced proficiency in SQL and Excel
• Experience using Python or R for data analysis and statistical modeling (regression, clustering, etc.)

Preferred qualifications include:

• Experience in architecting, implementing, and interpreting risk/scoring models in conjunction with data science teams
• Experience in high growth startups preferred
• Experience with visualization tools such as Tableau
• Experience building complex financial products and models

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Key responsibilities include: Developing best-in-class data-driven credit policies and business strategies, Delivering advancements in data and analytics, Improving credit monitoring and governance.

This role requires strong technical proficiency, a solid foundation in credit risk analytics, and the ability to turn complex data into actionable business strategies.

Location: This role is based out of Mexico City, Mexico, and is a full-time hybrid position where it is required to come into our office in Roma Norte (2–3 days/week).

#LI-HYBRID

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As a Credit Business Analyst, you will contribute to advancing our data and analytics framework, leveraging statistical methods and credit risk models to generate insights and drive strategy. You will also enhance credit monitoring and governance by building dashboards, improving reporting processes, and refining risk rating practices.

This role requires strong technical proficiency, a solid foundation in credit risk analytics, and the ability to turn complex data into actionable business strategies.

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You will contribute to advancing our data and analytics framework, leveraging statistical methods and credit risk models to generate insights and drive strategy. Additionally, you will enhance credit monitoring and governance by building dashboards, improving reporting processes, and refining risk rating practices.

This role requires strong technical proficiency, a solid foundation in credit risk analytics, and the ability to turn complex data into actionable business strategies.

Job Responsibilities:

• Develop best in class data-driven credit policies and business strategies: Leverage external (e.g. banking, tax, financial, credit bureau and other data) and internal performance data to develop credit policies (e.g. underwriting criteria, dynamic credit limit programs, etc.). Minimize credit loss by developing and implementing appropriate processes and procedures to identify and mitigate high risk customers.

• Deliver advancements in data and analytics: Contribute to the development of a data and analytics framework to improve processing, underwriting, tracking, risk management, and reporting procedures. Design and execute data driven analyses and tracking procedures to enhance insights on credit risk for individual, and the portfolio of companies.

• Improve credit monitoring and governance: Build dashboards to monitor portfolio health and strategy performance. Develop and implement improvements to credit portfolio monitoring, client review tracking, management reporting, and customer risk rating assignments

Required Qualifications:

• 2+ years in analytics within credit risk management.

• Fluent in English.

• Excellent written and verbal communication skills.

• Intellectual curiosity.

• Ability to translate complex data and model results into actionable business strategies.

• Proficiency in SQL and Excel.

• Experience using Python or R for data analysis and statistical modeling (regression, clustering, etc.)

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The homeowner funnel, from first impression through signed agreement and activated listing, is the highest-leverage product surface in the business. Most growth product roles are about optimizing what already exists: faster page loads, shorter forms, better copy. This role is about building something structurally different.

Belong's homeowner acquisition funnel is being rebuilt as an AI-native system: conversational intake powered by LLMs, personalized onboarding that adapts dynamically to each homeowner's financial profile, predictive scoring that routes the right lead to the right moment in the Advisor workflow, and agentic follow-up that replaces manual sequences with intelligent, context-aware outreach.

The target is a funnel that learns, where every interaction generates signal that makes the next interaction more likely to convert. As Product Owner, Growth, you are the person building that system. You own the homeowner acquisition and activation funnel end to end, from first contact to listed home.

Responsibilities ================

AI-native intake and qualification layer

The first interaction a homeowner has with Belong, whether via belonghome.com, a paid channel, or a referral, is where trust is either established or lost. You will build conversational intake flows powered by LLMs that qualify, capture, and begin converting leads in real time.

These are not chatbots with decision trees. They are context-aware systems that understand the difference between a cashflow-positive homeowner who wants yield optimization and a cashflow-negative homeowner who needs a path to profitability, and adapt the conversation, the framing, and the call-to-action accordingly.

Personalized onboarding and trust architecture

A homeowner considering Belong is anxious. They are considering handing over their most valuable asset to a platform they found online. Conversion at this stage is not a UX problem. It is a trust architecture problem.

You will design onboarding sequences that adapt dynamically based on homeowner attributes: property type, cashflow profile, prior rental history, risk signals, and behavioral signals from in-session activity.

You will use LLMs to generate personalized content, market analyses, improvement ROI estimates, comparable listings, that makes the value proposition concrete and specific to their home, not generic.

Predictive lead scoring and Advisor routing

Belong's Advisors are the trust-critical human touchpoint in the homeowner funnel. Their time is finite and high-value. You will build the predictive infrastructure that scores every lead on conversion likelihood, property quality, and fit with Belong's ICP, and routes leads to Advisors with the context they need to have the right conversation immediately.

You will work with data science to train and evaluate these models, with RevOps to deploy them into the Salesforce workflow, and with Sales leadership to validate signal quality against actual close rates.

Agentic follow-up and nurture sequences

Most leads do not convert on the first contact. Today, nurture is a sequence of templated emails. The target state is an AI agent that monitors lead behavior, page views, document opens, return visits, session signals, and generates contextually appropriate, personalized outreach at the right moment, with the right frame, without a human initiating every touchpoint.

You will define the agent's decision logic, build the context retrieval pipeline, instrument the output quality, and iterate on conversion impact week over week.

Funnel instrumentation and the learning loop

An AI-native funnel without rigorous instrumentation is a black box. You will build the measurement architecture that makes every conversion decision traceable: which intake flow variant produced the lead, which scoring model routed it, which agent-generated touchpoint influenced the next action, which Advisor framing closed it.

You will design the feedback loops that push conversion signal back into model evaluation, prompt improvement, and scoring recalibration. The funnel gets smarter every week or it is not an AI-native funnel.

The activation gap: agreement to listed home

Signing the agreement is not growth. A listed home is growth. The conversion from signed agreement to activated listing is a product problem with high leverage: homeowners who do not complete inspection scheduling, who abandon the improvement process, or who sit in the pipeline without a live listing represent real lost revenue.

You will own the product layer that closes this gap, including AI-assisted improvement planning, proactive homeowner communication anchored to their cashflow profile, and predictive identification of homeowners at risk of churning before listing.

The AI Stack You Will Work With ===========================

• LLM-powered conversational intake with real-time lead qualification and cashflow profile detection
• Personalized content generation using property-level market data, comparable listings, and improvement ROI modeling
• Predictive lead scoring models trained on conversion, property quality, and ICP signals
• Agentic follow-up workflows with behavioral trigger logic and context-aware generation
• Retrieval-augmented generation for Advisor preparation: the right context, surfaced at the right moment before the call
• A/B testing infrastructure applied to AI-generated content variants, not just static copy

What Success Looks Like ====================== 90 days: The funnel is fully instrumented from first click to activated listing with conversion rates and drop-off points visible at each stage. An AI-assisted intake flow is in production and being tested against the baseline.

6 months: Lead-to-listing conversion is measurably above baseline. AI is integrated at a minimum of 3 funnel touchpoints with documented conversion impact per touchpoint. Advisor routing is scored, and the correlation between score and close rate is being tracked.

Year 1: The majority of homeowner outreach between first contact and agreement signing is AI-generated, with human Advisors focusing exclusively on trust-critical call moments. CAC on the supply side is trending down. Time-to-activation is compressing quarter over quarter.

Example KPIs You Will Be Held To ==================================

• Lead-to-listing conversion rate (the primary number)
• Cost per activated listing
• Time from first contact to listing live
• AI-assisted funnel touchpoint conversion impact, measured per touchpoint
• Advisor routing accuracy: scored lead close rate vs. unscored baseline
• Experiment velocity: instrumented tests shipped per month
• Homeowner CSAT at onboarding and inspection phases (the constraint: conversion gains cannot come at experience cost)

Who You Are ============ AI systems builder, not AI enthusiast. You have shipped LLM-powered product features in production. You understand prompt engineering, retrieval quality, latency tradeoffs, output evaluation, and model feedback loops. You think about AI systems the way a statistician thinks about models: with explicit assumptions, known failure modes

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You'll lead the strategy, design, and execution of our QA program across FTE and vendor teams. Early on, you may be hands-on in building frameworks, calibrating reviews, and establishing reporting. As the program scales, you'll build and lead a team responsible for quality reviews, calibration, and performance insights across Support.

You'll work cross-functionally with Support, Learning & Knowledge, Support Operations, Product, and Engineering to ensure quality insights directly drive training, workflow improvements, automation strategy, and product feedback.

Responsibilities

• Build and lead the Support QA program, including evaluation frameworks, scoring models, review workflows, and calibration processes.
• Hire, develop, and manage QA specialists or analysts as the program scales.
• Define quality standards across ticket support, technical troubleshooting, and customer communication.
• Establish QA coverage strategy across FTE and vendor support teams.
• Lead calibration programs to ensure consistent quality standards across reviewers, teams, and regions.
• Partner with Learning & Knowledge to turn QA insights into training, onboarding improvements, and coaching strategies.
• Partner with Support Operations to embed quality signals into dashboards, reporting, and performance frameworks.
• Define and evolve quality standards for AI-assisted support, including agent assist usage, automation handoffs, and AI-generated content quality.
• Utilize Replit to build internal tooling and recommend external tooling when necessary to improve QA workflows and program scalability.
• Define and track key quality metrics (QA trends, CSAT correlation, escalation rate, escalation rate, repeat contact rate, policy adherence) and report insights to Support leadership.

Requirements

• 5+ years in Support Quality, Support Operations, Technical Support, or similar roles in a technology company.
• 2+ years in a people management or team leadership capacity.
• Experience building or significantly evolving a QA program, framework, or evaluation system.
• Strong understanding of customer support workflows, ticket lifecycle, and escalation patterns.
• Experience working with support platforms (Zendesk or similar) and QA tooling or review workflows.
• Strong analytical mindset with experience using data to identify trends and drive performance improvements.
• Experience working cross-functionally with Support leadership, Operations, and Enablement or Training teams.
• Strong written and verbal communication skills, including delivering structured performance feedback and coaching guidance.
• Experience working in fast-moving product environments with frequent releases and evolving workflows.
• Hands-on experience using AI tools (e.g., Replit, Claude, ChatGPT, or similar) to improve workflows, knowledge creation, training, or support operations.
• Experience managing or influencing quality across vendor or BPO support environments.

Nice to Have

• Experience designing QA programs for technical or developer-focused support environments.
• Experience building QA programs that support both human and AI-assisted support interactions.
• Experience partnering on support automation, routing, or workflow design decisions.
• Experience building QA dashboards, reporting frameworks, or performance analytics models.
• Background in coaching, enablement, or performance improvement programs.
• Experience scaling QA programs in high-growth or early-stage environments.

Bonus Points

• Has used Replit and built internal tooling
• Experience working with IDEs, terminals, or other common developer tools.

What We Value

• Problem-solving mindset: Ability to approach complex operational challenges systematically and devise effective solutions
• Self-directed and autonomous: Capable of working independently while collaborating effectively with cross-functional teams
• Strong communication skills: Ability to explain complex technical concepts to both technical and non-technical audiences
• Continuous learning: Passion for staying current with industry best practices and new technologies
• Focus on automation: Strong belief in automating repetitive tasks and building self-healing systems

Full-Time Employee Benefits Include

• Competitive Salary & Equity
• 401(k) Program with a 4% match
• Health, Dental, Vision and Life Insurance
• Short Term and Long Term Disability
• Paid Parental, Medical, Caregiver Leave
• Commuter Benefits
• Monthly Wellness Stipend
• Autonomous Work Environment
• In Office Set-Up Reimbursement
• Flexible Time Off (FTO) + Holidays
• Quarterly Team Gatherings
• In Office Amenities

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