About the job
About the Team You'll Join
- The Data Analyst at Toss Bank is integrated into both the squad and DA chapter, functioning within a matrix structure.
- At Toss Bank, teams are organized into squads, consisting of 6-8 professionals including Product Owners, Designers, Developers, and Analysts, working autonomously like a startup.
- Data Analysts in the Toss community belong to the DA chapter, which meets every Wednesday to share insights on data analysis topics.
- The DA chapter comprises team members with 1 to 12 years of experience from diverse backgrounds, including finance, corporate, gaming, and e-commerce.
Key Responsibilities You'll Undertake
As a Data Analyst at Toss Bank, you will define business problems through data and identify optimal solutions to effect real change.
- Drive Data-Driven Decision Making:
- Go beyond merely extracting requested data by defining the 'real problems' behind business needs and formulating hypotheses.
- Validate hypotheses using appropriate methodologies, such as causal inference and A/B testing, to derive actionable insights.
- Create Reliable and Efficient Analytical Environments:
- Focus on developing reusable data structures that team members can utilize repeatedly, moving beyond one-time analyses.
- Establish data standards through data mart design and consistency validation that everyone can trust.
- Work Smart with Technology:
- Utilize cutting-edge technologies (AI, LLM, etc.) to enhance efficiency in repetitive tasks, allowing for a focus on more significant problem-solving.
- Proactively adopt new analytical methods and tools that can elevate productivity without being constrained by past practices.
We Seek a Candidate Who:
- Can discern the essence of problems and proactively propose solutions that consider the impact on our team, company strategy, and business objectives.
- Understands complex business logic and can translate this into coherent SQL queries for structured data.
- Is adept in deep data analysis using tools like Python/R, capable of accurately interpreting experimental results based on statistical knowledge.
- Can communicate complex analytical findings in simple terms that non-experts can understand and persuade.
Preferred Experience:
- Experience in constructing dashboards and analytical environments using tools such as Tableau or Amplitude is a plus.
- Prior involvement in service planning and log design stages is advantageous.
- Experience in leveraging new technologies (like AI) to enhance work processes is preferred.
Resume Tips:
- Detail impactful projects you have worked on.
- Showcase the process of problem definition, hypothesis formulation, experimental design, validation, and results.
- Mention any experiences that led to deep user understanding through data analysis and actionable insights.
- Check if you have extensively utilized mobile service data analysis methods (LTV, AARRR, Cohort, Funnel, etc.).