Staff Sales Analyst

Mulligan Funding•San Diego, CA

35d•$131,700 - $170,900

About The Position

Mulligan Funding is a San Diego-based fintech lender serving small and medium-sized businesses. The Staff Sales Analyst will work at the intersection of Sales, Operations, Data Engineering, and Data Science, serving as a source of actionable sales insights and funnel metrics. The role defines and tracks sales funnel health, measures team performance with frontline operations, and partners with technical teams to scale data pipelines and predictive models. The company seeks an AI-native practitioner who is eager to adopt and experiment with modern AI tools, automated workflows, and generative AI to accelerate insight delivery and transform sales operations. The position focuses on optimizing conversion, velocity, and revenue; measuring operational performance and friction; supporting data infrastructure and machine-learning initiatives; and embedding new analytical and AI capabilities in day-to-day operations. The role is based in San Diego, California. The stated base compensation estimate is $131,700 to $170,900 per year. Total compensation may include base salary and other compensation, such as an annual bonus, as applicable. Mulligan Funding offers medical, vision, and dental benefits; FSA; 401(k) matching; sick, vacation, and holiday benefits; a gym membership contribution; an internal referral program; and company events. The company is an Equal Opportunity Employer.

Requirements

Undergraduate degree (B.A. or B.S.) in Business Analytics, Finance, Economics, Statistics, Information Systems, or a related quantitative field.
5–7+ years of experience supporting Sales, Marketing, or Revenue Operations in an analytical capacity.
Proven track record of driving strategic operational change.
Highly proficient in advanced Excel, including complex formulas, pivot tables, scenario modeling, and dynamic reporting constructs.
Exceptional written and verbal communication skills.
Ability to constructively challenge assumptions, advocate for data-driven decisions, and articulate complex insights to executive stakeholders.
Curiosity-driven, AI-first approach to problem-solving, process efficiency, and continuous learning.

Nice To Haves

Experience in fintech, financial services, or a high-growth B2B environment is preferred.
Exposure to Python for data manipulation (Pandas, NumPy) and workflow automation.
Experience querying relational databases or building dashboards with Tableau, Power BI, or Looker.

Responsibilities

Produce actionable insights to optimize conversion, velocity, and revenue across acquisition and sales channels.
Design, build, and maintain core metrics and KPIs that define end-to-end sales funnel health, including lead-to-application, approval-to-acceptance, funded rates, and SLA timelines.
Conduct diagnostic deep dives to identify friction points, application drop-offs, and operational bottlenecks.
Work with Sales and Operations leadership to understand frontline workflows, customer journeys, and deal dynamics.
Partner with Sales and Operations leaders to build consensus and deliver deal-prioritization and pipeline-optimization recommendations.
Manage day-to-day and deal-by-deal operational friction, translating feedback and business requirements into analytical frameworks and executive dashboards.
Establish measurement frameworks for team performance, incentive structures, channel efficiency, and capacity planning.
Bridge business stakeholders, Sales leadership, and technical teams to align business priorities with technical execution.
Collaborate with Data Engineers and Data Scientists to productionalize metrics and support robust, single-source-of-truth data infrastructure.
Help scope, validate, and test scalable machine-learning models and analytical frameworks to improve funding conversion and targeting.
Lead adoption, testing, and scaling of AI technologies, including LLM-based analytics assistants, automated insight generation, prompt engineering tools, and conversational analytics platforms.
Automate routine reporting and data extraction workflows to reduce manual effort across business units.
Evaluate emerging technologies and embed advanced analytical capabilities into day-to-day operations.