Looking for a curious and willing to take action analyst with a passion for protecting consumers from fraudulent communications. The ideal candidate is comfortable navigating sophisticated problems, resolving right data to focus on and able to derive understanding and recommendations based on data. This role interacts with partners throughout the organization in helping advise product, risk, and operations strategy.
What you’ll do:
- Work with product managers to inject data into decision making and planning to increase confidence in anti-fraud outcomes via defining important metric, crafting and building reports, dashboards, and assisting with feature ideas for data science teams building predictive models.
- Work closely with the extended fraud operations team to identify current fraud trends, provide insights into fraud issue-priority from aggregated data and dashboards, and generally serve as a data-partner to operations teams.
- Communicate anti-fraud insights and recommendations to partners at all levels via standardizing and maintaining metrics, models, datasets, and dashboards.
What you should have:
- 3+ years of professional experience doing quantitative analysis, building dashboards and reports, working with business partners to convert ambiguous, messy data into tangible and meaningful results that influence anti-fraud decisions in a fast paced industry.
- Ability to think strategically and initiate, refine, and complete projects with minimal mentorship in an often ambiguous environment.
- Excellent verbal and written communication skills.
- Expert level experience working with SQL and relational data (3+ years on a regular basis). Experience with unstructured data is a plus.
- Experience with data visualization. Experience with Tableau and Looker (LookML) is preferred.
- Demonstrated ability in building reports, metrics, dashboards, etc. for analytics projects related to data preparation processes such as data extraction, cleansing, mapping, loading and operationalizing workflows in production.
- Experience in telephony, email, messaging, social media, or other electronic communications
- Experience in fraud / abuse prevention and investigation
- Experience or familiarity with data warehousing and/or ETL processes, and distributed computing (Hive/Presto)
- Experience in python (e.g. pandas, numpy, scikit-learn) or R for data manipulation and analysis, including predictive analytics, regression, hypothesis testing, and causal analysis