Timo’s data volume and stakeholder demand are growing faster than the current Data Analyst team’s capacity, driven by expansion across lending (PayLater/Timo Credit), deposits (GoalSave, Term Deposit, CD), and payments.

This role creates a senior layer within the team: a technically strong analyst who can own complex, ambiguous problems end-to-end, represent data insights directly to leadership and cross-functional stakeholders, and manage a small pod of junior analysts — freeing the Head of Data to focus on strategic and cross-departmental priorities.

A day in your life might include

1. Data Analysis & Technical Delivery

• Own end-to-end analysis for high-priority initiatives (e.g., lending, deposits, cards, new user onboarding), from framing the business question to delivering the recommendation.

• Write efficient, well-documented SQL and Python (pandas) to extract, clean, and model data from the Group’s warehouse (Athena, dbt).

• Build and maintain dashboards and self-serve reporting (Holistic Dashboard) that reduce ad-hoc requests to the team.

• Design and evaluate experiments (A/B tests), cohort, funnel, and segmentation analyses to support product and growth decisions.

• Own, set, and enforce data quality, documentation, and analytical rigor standards and event tracking across the team’s outputs.

2. Insight Generation & Stakeholder Communication

• Translate complex, technical analysis into clear, decision-ready narratives for non-technical audiences, including senior management.

• Present findings and recommendations directly to stakeholders across Product, Business, Risk, and Compliance — including trade-offs, confidence levels, and limitations.

• Partner with Product and Business teams to define success metrics and KPIs for new initiatives before launch, not just measure them after.

• Proactively surface trends, risks, and opportunities from data, rather than only responding to inbound requests.

3. Team Leadership & Capability Building

• Manage and mentor a small pod of junior Data Analysts: allocate work, review quality, and support their technical and career development.

• Establish and maintain team best practices — coding standards, QA checklists, and analysis templates — to keep output consistent as the team scales.

• Act as the working-level bridge between the Head of Data and junior analysts: unblock issues, manage delivery timelines, and escalate where needed.

• Contribute to onboarding new analysts and to the team’s broader learning & development plan.

How to succeed in this role

Must-Have

• Bachelor’s degree in Statistics, Economics, Computer Science, Data Science, or another quantitative field.

• 4–6 years of hands-on data analysis experience, ideally in fintech, banking, or a fast-scaling tech/e-commerce environment.

• Strong SQL and proficiency in Python (pandas) for data manipulation and analysis.

• Working experience with BI/visualization tools (e.g., Metabase, Redash, Tableau, Power BI, Holistics, Mode, or similar).

• Proven track record of presenting data-driven recommendations to senior stakeholders, with strong written and verbal communication.

• Some prior experience mentoring, coaching, or informally leading junior analysts.

Nice-to-Have

• Experience with modern data warehousing and transformation tools (Athena, BigQuery, Redshift, Snowflake, dbt, etc.).

• Familiarity with product/CRM analytics platforms (Amplitude, MoEngage, AppsFlyer, Firebase).

• Exposure to lending, payments, or digital banking data and regulatory context.

• Experience designing and interpreting statistical experiments (A/B testing).

Core Competencies

• Critical thinking: questions assumptions and framing, not just the request as written — gets to the real business problem.

• Executive communication: can simplify complex analysis into a clear recommendation for a non-technical, senior audience.

• People management fundamentals: coaching, feedback, prioritization, and accountability for a small team’s output.

• Stakeholder management: comfortable working across Product, Business, Risk/Compliance, and occasionally external partners.

Success Measures – First 2 Months

• Independently owns and delivers analysis for at least one major cross-functional initiative.

• Junior analyst pod is fully onboarded, with clear work allocation and a visible reduction in the Head of Data’s day-to-day oversight load.

• At least one recurring manual/ad-hoc reporting need converted into a self-serve dashboard.

• Demonstrated ability to present directly to senior stakeholders without needing the Head of Data as a buffer.