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Data / Data Mining Analyst
La Crescenta-Montrose, CA
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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
