Kredivo ★ 4.0

The Data Analyst in the Customer Department plays a critical role in transforming raw data into actionable insights that enhance customer experience, improve key customer metrics, and support strategic decision-making across the organization. This position requires strong analytical skills, high data sensitivity, technical capability, and cross-functional communication.

Responsibilities

Data Analysis & Reporting

• Collect, extract, and process data from multiple internal sources using tools such as SQL, Python, and other data pipelines.
• Build, automate, and maintain dashboards to monitor key Customer metrics (Retention, CSAT, FCR, SLA, Ticket Volume, Churn risk, etc.).
• Monitor daily/real-time data to detect anomalies and proactively raise alerts when issues arise.
• Conduct deep-dive analyses to identify root causes and provide data-driven recommendations for Operations, Product, Risk, and other teams.

Forecasting & Data Modeling

• Develop forecasting models for ticket volume, churn, and customer lifecycle trends.
• Formulate hypotheses based on observed data patterns and validate them using statistical methods.
• Support business initiatives with insights, data simulations, and scenario modeling.

Cross-functional Collaboration

• Work closely with Product, Marketing and other teams to help them understand data and its implications for decision-making.
• Translate complex datasets into clear, simple, and visually compelling insights that stakeholders can easily understand.
• Participate in customer experience improvement initiatives and process optimization projects driven by data.

Qualifications

• 1–3 years of experience as a Data Analyst, BI Analyst, Customer Analyst, or related role.
• Experience in fintech, banking, or digital product environments is a strong advantage.
• Proficient in SQL and Python with experience writing and optimizing complex code.
• Hands-on experience with data visualization tools such as Power BI, Looker Studio, Tableau, Mode or Metabase.
• Good understanding of statistics, forecasting methods, cohort analysis, and customer analytics.
• Strong attention to detail with high sensitivity to numbers and data patterns.
• Excellent analytical thinking and problem-solving skills; able to propose solutions grounded in data.
• Effective cross-functional communication; able to explain data and analytical findings to non-technical audiences.
• Proactive, structured, detail-oriented, and able to work independently.
• Strong English communication skills (both written and verbal) to collaborate effectively with cross-functional teams and stakeholders.