Yêu cầu công việcWhat You&039;ll Bring
Advanced programming skills in Python or R for data manipulation, statistical analysis, and automation are highly desirable. Experience with relevant data science libraries (e.g., Pandas, NumPy, Scikit- learn) is also highly desirable.
Proven experience designing and implementing sophisticated data visualization solutions using tools like Tableau, Power BI, or similar.
A proactive and strategic mindset with a passion for leveraging data to solve challenging problems and drive business value.
Demonstrated ability to translate business questions into analytical frameworks and deliver actionable insights that drive measurable results.
Strong understanding of statistical principles, data modeling techniques, and experimental design. Experience with machine learning concepts is a significant plus.
Experience mentoring and guiding junior team members.
Strong problem- solving and critical- thinking skills with a proven ability to independently investigate and resolve complex data- related issues.
Minimum of 5+ years of progressive experience in data analysis, preferably with a focus on fraud detection, risk management, or a related domain.
Expert- level proficiency in database technologies (SQL, NoSQL, data warehousing concepts) with the ability to write highly optimized and complex queries.
Excellent communication and presentation skills, with the ability to effectively communicate complex technical concepts to both technical and non- technical audiences.
Proven ability to work independently, manage multiple projects simultaneously, and prioritize effectively in a fast- paced environment.
Bachelor&039;s degree or higher in Computer Science, Mathematics, Statistics, Economics, or a related quantitative field. Master&039;s degree preferred.
Bonus Points For:
Contributions to data analytics communities or open- source projects.
Experience in online gaming, financial services, or other high- risk/regulated industries.
Experience building and deploying statistical models or machine learning algorithms.
Experience with cloud- based data platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop).
Deep understanding of specific anti- fraud techniques, technologies, and industry best practices.