Customer Audit Data Science Specialist

4 days ago


Penang, Malaysia Intel Corporation Full time

Job Description

The Customer Audit Data Science Specialist will be part of our Global Customer Audit Team, responsible for developing and implementing customer compliance risk AI and Machine Learning models. This is an exciting opportunity for a graduate with a Bachelor's degree in Computer Science to kickstart their career in a dynamic and innovative environment.

About Our Team

We work closely with cross-functional teams to derive insights from big data analytics from both internal and external data. Our team focuses on creating and maintaining end-to-end applications leveraging AI and Machine Learning that support our anomaly detection and risk sensing efforts.

Main Responsibilities

  1. Model Implementation: Support in the development and implementation of customer compliance risk AI and Machine Learning models on evolving internal and external environments. Monitor model accuracy and recommend changes as needed.
  2. Data Validation: Partner with IT and data scientists on key model development and productization tasks, such as data engineering, data validation, model testing, and documentation.
  3. Market Analysis: Gather relevant market intelligence through web scraping and sentiment analysis.
  4. Data Visualization: Analyze large datasets to extract actionable insights and present findings to stakeholders using PowerBI.
  5. Automation Efficiency: Explore and execute automation opportunities on key audit analyses/tasks to generate efficiencies.
  6. Database Management: Utilize SQL for database management and data manipulation.
  7. Technological Advancements: Stay up-to-date with the latest advancements in AI, Machine Learning, and data science technologies.
  8. Mentorship and Coaching: Coach and mentor junior risk analysts and other team members learning data science, gen AI, and automation tools.

Qualifications and Experience

  1. Bachelor's degree in Computer Science.
  2. Minimum 2 years of working experience.
  3. Strong foundation in AI, Machine Learning, and data science concepts.
  4. Proficiency in programming languages such as Python, SQL.
  5. Experience with data analysis and visualization tools (e.g., Pandas, NumPy, Plotly, PowerBI).
  6. Familiarity with SQL for database management and data manipulation.
  7. Familiarity with software engineering practices, including version control (e.g., Git) and agile methodologies.
  8. Excellent problem-solving skills and the ability to work independently and collaboratively.
  9. Strong communication skills and the ability to present complex technical information to non-technical stakeholders.
  10. Passion for continuous learning and staying current with industry trends.


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