Job Description
As a Data Analyst (intern), you will support the team in automating analytics and reporting pipeline to improve the speed of delivery and quality of insights. You will assist in the documentation of work instructions (Standard Operating Procedures) to ensure that processes can be well-adhered to and errors can be mitigated. You will participate in problem solving of key operational challenges or brainstorming of ideas to improve operational performance.
Key Responsibilities:
- Shadow Operations staff to understand As-Is process and key data challenges
- Leverage proprietary and/or open-source technologies to automate data preparation pipeline
- Study user requirements and ensure that codes are optimized
- Design test cases to detect bugs and ensure robustness of codes
- Review codes and ensure commenting is adequate for ease of maintenance
- Create and/or maintain documentation for data dictionary and data lineage
- Develop flowcharts to represent actual processes, actors and timelines
- Conduct in-depth data analysis to generate insights, identify issues and facilitate decision-making
- Design and build dashboards to track business metrics
- Collaborate with cross-functional teams to understand business needs and provide data-driven recommendations
- Scan and experiment with emerging AI tools, libraries and techniques, assessing their applicability to Income’s context
Requirements:
- Undergraduate in Data Science, AI, Statistics or a related field
- Demonstrated ability to design and develop mid-scale data preparation pipeline using proprietary or open-sourced technologies
- Competent in SQL and Python
- Possess debugging skills and keen eye for code defects
- Understanding of machine learning concepts and generative AI / Large Language Models
- Experienced in using analytics and/or solution delivery software, e.g. Tableau, Alteryx, Github
- Familiar with software development life cycle process, agile methodology and/or lean principles
- Strong analytical skills and problem-solving mindset
- Collaborative team player
