Job DescriptionAs a Data Analyst, you'll work closely with our Fraud and Risk team to analyze complex datasets, develop insights, and contribute to data-driven decision-making across the organization.
Never Ordinary. Are you interested in the fast-paced and growing sports gambling industry? We're seeking a Fraud and Risk Analyst to join our team at our US headquarters in downtown Denver!
The Fraud and Risk Management department plays a crucial role in protecting the company from financial threats and fraudulent activities. The key responsibilities include: Fraud Prevention and Detection, Risk Assessment and Management, Compliance and Security.
You will work closely with management tiers to implement reporting structures and data driven solutions, which advance our risk prevention measures.
This is not just a job; it's a career opportunity for aspiring data scientists to get hands on experience and apply their skills to implement solutions.
This position is full time and demands technical ability, statistical knowledge and data visualization techniques.
Qualifications- Degree in Computer Science, Statistics, Mathematics, Economics or similar discipline.
- Proficient in the application and use of Python is a prerequisite of this role.
- Experience in programming languages such as SQL or R is desirable.
- Basic understanding of data science principles and methods.
- Ability to deliver solutions through quantitative research using multiple data sources.
- Self-motivated to research and deliver change within the department.
- Strong analytical and problem-solving skills
- Ability to effectively communicate with staff throughout the operation, up to and including key stakeholders; identifying and undertaking the appropriate course of escalation.
Additional Information- Collect, process, and analyze large datasets using various tools and techniques.
- Developing and maintaining dashboards and other data products.
- Instilling accurate and effective reporting channels.
- Presenting research and analysis in a clear and concise manner.
- Assist in the creation and implementation of rules-based or machine learning models.
- Collaborate with cross-functional teams to identify business problems and propose data-driven solutions
