EchoStar's Retail Wireless team is looking to prevent, detect, and mitigate device fraud through advanced data analysis and investigation.
Requirements
- Strong proficiency in Python is required
- Experience with Machine Learning tools is required
- Proficiency in SQL for data querying and manipulation
- Experience with data visualization tools (e.g., Tableau, Power BI)
- Proficiency in Excel for data analysis and reporting.
- Knowledge of fraud detection techniques, algorithms, and methodologies
- Experience with waterfall methodology to display reporting trends
Responsibilities
- Leverage Python and Machine Learning for the automation of reporting processes and enhancement of operational efficiency
- Utilize SQL for data extraction and reporting to bolster fraud detection and prevention efforts.
- Creation of Tableau dashboards to identify suspicious patterns, anomalies, and trends indicative of device fraud
- Perform data analysis on large datasets within Excel/Sheets to discern trends, anomalies, and patterns indicative of fraudulent behavior.
- Develop and maintain reports to track key fraud metrics, monitor performance, and communicate findings to stakeholders
- Stay up-to-date on emerging fraud trends, techniques, and technologies, and proactively identify new fraud threats and vulnerabilities
- Collaborate with cross-functional teams to develop, implement, and refine fraud prevention rules and algorithms based on data-driven insights and analysis
Other
- At least 2-3+ years of experience in a similar role, preferably in data analytics, business analytics, fraud analytics, or data science
- At least 1+ year(s) of experience conducting Machine Learning
- Strong analytical and problem-solving skills, with the ability to think critically and creatively to uncover fraud patterns and anomalies
- Excellent communication and presentation skills, with the ability to effectively convey complex technical concepts to non-technical stakeholders
- Detail-oriented with strong organizational skills and the ability to manage multiple priorities and deadlines
