The job description is comprehensive and well-structured, but it could benefit from clearer segmentation and some minor formatting improvements for enhanced readability. Here's a refined version:
Qualifications
- Master's Degree in Mathematics, Statistics, Economics, or other quantitative disciplines
- 8-10 years of research, data science, or analytical modeling experience in relevant domains
- Familiarity with:
- Statistical and technical analysis tools (R, Python, SQL, SAS, Cognos, etc.)
- Automated machine learning tools (Matplotlib, RapidMiner, DataRobot)
- Retail banking or transaction processing industries
Technical Skills
- Strong quantitative background for statistical analyses and coding
- Fluency in risk analytics, pricing, risk modeling
- Experience with pattern recognition or machine learning
- Data mining, manipulation, analysis, visualization, and reporting tools
- Handling large datasets; experience with distributed computing tools (Map/Reduce, Hadoop, Hive) is a plus
Research and Analytical Mindset
- Ability to structure projects from idea to implementation
- Manipulate and analyze complex, high-volume, high-dimensional data from diverse sources
- Passion for empirical research and data-driven problem solving
- Flexible analytical approach for varying levels of result precision
- Effective communication of complex analyses to non-technical stakeholders
Personal Attributes
- Driven, focused self-starter with excellent communication and follow-through skills
What Your DNA Looks Like
- Passion to win: Thrive in competitive environments and aim to be a market leader
- Communicative: Strong interpersonal skills and ability to present information effectively
- Resourceful: Independent worker with a problem-solving mentality
- Organized: Ability to prioritize tasks and work with a sense of urgency
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