Front-Facing Data Analyst

We are seeking a highly analytical and business-savvy Front-Facing Data Analyst to serve as the bridge between our data science team and external stakeholders.

This role combines strong technical data skills with client communication, strategic insight, and performance optimization expertise.

The ideal candidate is comfortable analyzing complex datasets, translating findings into actionable insights, and presenting results directly to clients, partners, and executive teams.

Key Responsibilities: Client & Stakeholder EngagementServe as the primary analytics point of contact for clientsPresent performance reports, dashboards, and model insightsTranslate complex statistical results into clear business recommendationsParticipate in strategy calls Data Analysis & ReportingAnalyze large datasets to identify trends, risks, and opportunitiesBuild and maintain dashboards (Tableau, Power BI, Looker, etc.)Monitor KPIs such as approval rates, conversion, fraud indicators, and portfolio performanceSegment performance by channel, risk tier, geography, or campaignPerform root cause analysis on performance shifts Modeling & Optimization SupportWork alongside data scientists to interpret model outputsConduct A/B testing and performance experimentsIdentify segments with low performance and recommend optimization strategiesValidate model deployment performance in production environments Cross-Functional CollaborationCoordinate with underwriting, marketing, risk, and engineering teamsAssist in translating business requirements into analytical frameworksSupport compliance and reporting needs where applicable Required QualificationsBachelor’s degree in Statistics, Mathematics, Economics, Computer Science, or relatedfield2–5+ years of experience in data analytics, preferably in fintech or lendingStrong proficiency in SQLExperience with R or PythonExperience with BI tools (Tableau, Looker, etc.)Excellent communication and presentation skillsAbility to explain technical concepts to non-technical audiences Preferred QualificationsExperience with underwriting analytics or credit risk modelingFamiliarity with interpretable ML models (scorecards, gradient boosting, etc.)Knowledge of fraud detection metricsExperience working in a client-facing or consulting environment Core CompetenciesStrong analytical thinkingBusiness acumenExecutive-level communicationDetail-oriented but able to see big pictureProactive problem-solving mindset