Job Description
CTO/CCT/DATA/C429/200417
Job Responsibilities
Scope of Consultancy Services:
- Weekly consultancy in algorithm review and system enhancement for machine learning platform and the modules which may lead to patentable results on financial risk analysis;
- Suggest potential performance benchmark model and methodology for improvement;
- Consultancy on industry direction and academic research scope. Help formulate and focus research and development direction on Data Analytics, especially in leveraging graph analytics for solving business problems such as recommendation, fraud detection and risk management.
- To leverage existing expertise and know-how in big data analytics focusing on leveraging graph analytics for risk analysis and anomaly detection;
- Two technical lectures will be given in the area of
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- The technical trend for pattern discovery and anomaly detection;
- Uncertain data modelling, queries, and analysis
- Solving the technical problems, which includes checking algorithm results, finding out the cause of the problem and improvement solution and providing a final report for the requirement.
Requirements
- PhD. Degree in Computer science or related disciplines;
- 10+ years of academic research experience in database, data management and/or big data analytics is highly preferred.
- Strong publication in world top conferences/journals in database and data mining such as SIGMOD, VLDB, ICDE, KDD, IEEE TKDE, VLDBJ, etc is highly preferred;
- International recognition in research community, as evident by holding important positions (e.g., associate editor in top journals, steering committee members, PC chairs, area chairs, or PC members in top conferences) is highly preferred.
- Strong mathematical modelling and performance analysis capability in graph data is highly preferred;
- Broad knowledge and connection in the community – keep abreast of the state of art of the latest progress, other industry players approach and academic R&D direction is highly preferred
- Good understanding and appreciation of algorithmic level fine tuning versus system-level performance optimization – various tradeoff, parameters, adjustment, and modeling effects etc is highly preferred
Application
Interested candidates please send application (quoting Ref. No.) with detailed resume, current and expected salary to Talent Acquisition via email to [email protected]
Only short-listed candidates will be notified. ASTRI reserves the right not to fill the position.
ASTRI is an Equal Opportunities Employer. Personal data provided by job applicants will be used exclusively for recruitment only.