Data Science Risk Analyst

2 weeks ago


Kuala Lumpur, Kuala Lumpur, Malaysia Krisvconsulting Services Pte Ltd Full time

We are currently seeking a highly skilled Data Science Risk Analyst to support our team in developing and maintaining scalable data solutions for risk assessment and mitigation. The ideal candidate will have a strong foundation in Hadoop and SQL Server, with a focus on building and maintaining risk data models to support data-driven risk management strategies.

Key Responsibilities:
  • Analyze and interpret complex data sets related to risk factors to ensure accurate risk assessments and data integrity.
  • Design, implement, and maintain risk data models using Hadoop ecosystems, including HDFS, MapReduce, Hive, and Pig.
  • Develop, manage, and optimize databases, data warehouses, and ETL processes on SQL Server to support risk data analytics.
  • Collaborate with data science and business teams to integrate data-driven insights into risk management strategies.
  • Perform data cleansing, data mining, and statistical analysis to support decision-making in risk mitigation.
  • Create and automate reports and dashboards for key risk metrics, leveraging SQL, Hadoop, and data visualization tools.
  • Monitor and maintain data pipelines, ensuring seamless data integration and availability for risk assessment purposes.
  • Troubleshoot and resolve performance issues in data systems and risk analytics platforms.
  • Document technical processes, workflows, and data transformation methodologies to ensure traceability and transparency in data management.
Required Qualifications:
  • Bachelor's degree in Computer Science, Information Technology, Data Science, or a related field.
  • 3-5 years of experience working in data analysis, system analysis, or risk management in a data-driven environment.
  • Proficiency in Hadoop ecosystem tools (HDFS, MapReduce, Hive, Pig, Spark) and experience in managing large data sets.
  • Strong expertise in SQL Server, including database design, query optimization, and ETL process development.
  • Proven ability to analyze, model, and interpret complex datasets to generate actionable risk insights.
  • Strong knowledge of data governance, data quality, and risk management principles.
  • Excellent communication skills with the ability to explain technical concepts to non-technical stakeholders.
Preferred Qualifications:
  • Experience with risk modeling or quantitative analysis in financial services, insurance, or a related industry.
  • Knowledge of scripting languages like Python or R for data analysis.
  • Familiarity with big data tools like Spark, Kafka, or Cloudera.
  • Experience with data visualization tools (Tableau, Power BI, etc.) for risk reporting.


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