As a Senior Data Scientist, you will be responsible for developing analytical and predictive models that directly influence product, marketing, and revenue decisions.
You will work with raw data, design analytical frameworks, identify behavioral and financial patterns, and transform data into decision-making tools used by management and operational teams.
This role combines deep analytical thinking, strong technical skills, and practical business orientation.
Data Analysis & Pattern Discovery
• Explore large datasets to identify behavioral, product, and revenue patterns.
• Detect structural effects, hidden dependencies, and non-obvious drivers.
• Perform variance and factor analysis across business metrics.
• Investigate anomalies, outliers, and unexpected metric movements.
Predictive Modelling & Forecasting
• Develop predictive models (LTV, churn, demand, revenue drivers, etc.).
• Build regression, clustering, and time-series models.
• Design conditional forecasts and scenario-based projections.
• Evaluate model stability, accuracy, and business relevance.
Decision Support & Diagnostic Tools
• Build analytical and diagnostic tools for business and product decisions.
• Design monitoring systems and early-warning indicators.
• Translate business problems into analytical models.
• Support hypothesis testing and experiment design.
Data Infrastructure & Quality
• Write and optimise SQL queries for analytical workloads.
• Contribute to data structure, logic, and metric definitions.
• Validate data reliability, consistency, and completeness.
• Handle imperfect datasets, sampling issues, and missing data.
Visualization & Reporting
• Build dashboards and analytical monitors in Power BI.
• Present analytical findings in clear, decision-oriented formats.
• Transform complex data into actionable insights.
Cross-Functional Collaboration
• Partner with Product, Marketing, Finance, and BI teams.
• Support strategic and operational decisions with data analysis.
• Drive analytical thinking across the organization.
• SQL queries and optimisation
• Database design fundamentals
• Python (Jupyter, pandas, matplotlib)
• Data collection via APIs, Google Analytics, web and open sources
• Data visualisation and dashboard design (Power BI)
• Machine Learning: regression, clustering, time series, variance analysis
• Statistical and mathematical foundations for data analysis
• Data quality assessment and validation
• Designing analytical experiments
• Working with metrics and KPI systems
• Understanding behavioural economics, product usage, and funnels
