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 Engagement
• Serve as the primary analytics point of contact for clients
• Present performance reports, dashboards, and model insights
• Translate complex statistical results into clear business recommendations
• Participate in strategy calls

• 🔹 Data Analysis & Reporting
• Analyze large datasets to identify trends, risks, and opportunities
• Build and maintain dashboards (Tableau, Power BI, Looker, etc.)
• Monitor KPIs such as approval rates, conversion, fraud indicators, and portfolio performance
• Segment performance by channel, risk tier, geography, or campaign
• Perform root cause analysis on performance shifts

• 🔹 Modeling & Optimization Support
• Work alongside data scientists to interpret model outputs
• Conduct A/B testing and performance experiments
• Identify segments with low performance and recommend optimization strategies
• Validate model deployment performance in production environments
• 🔹 Cross-Functional Collaboration
• Coordinate with underwriting, marketing, risk, and engineering teams
• Assist in translating business requirements into analytical frameworks
• Support compliance and reporting needs where applicable

• Required Qualifications
• Bachelor’s degree in Statistics, Mathematics, Economics, Computer Science, or related
• field
• 2–5+ years of experience in data analytics, preferably in fintech or lending
• Strong proficiency in SQL
• Experience with R or Python
• Experience with BI tools (Tableau, Looker, etc.)
• Excellent communication and presentation skills
• Ability to explain technical concepts to non-technical audiences

• Preferred Qualifications
• Experience with underwriting analytics or credit risk modeling
• Familiarity with interpretable ML models (scorecards, gradient boosting, etc.)
• Knowledge of fraud detection metrics
• Experience working in a client-facing or consulting environment

• Core Competencies
• Strong analytical thinking
• Business acumen
• Executive-level communication
• Detail-oriented but able to see big picture
• Proactive problem-solving mindset