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Front-Facing Data Analyst
Job in
Glendale, Los Angeles County, California, 91222, USA
Listed on 2026-06-03
Listing for:
Plat.AI
Full Time
position Listed on 2026-06-03
Job specializations:
-
IT/Tech
Data Analyst, Data Science Manager
Job Description & How to Apply Below
Overview
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
- Present performance reports, dashboards, and model insights
- Translate complex statistical results into clear business recommendations
- Participate in strategy calls
- 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
- Work alongside data scientists to interpret model outputs
- Conduct A/B testing and performance experiments
- Identify segments with low performance and recommend optimization strategies
- Coordinate with underwriting, marketing, risk, and engineering teams
- Assist in translating business requirements into analytical frameworks
- Support compliance and reporting needs where applicable
- 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
- 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
- Strong analytical thinking
- Business acumen
- Detail-oriented but able to see big picture
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