Data Analyst – Retail Analytics

Overview

Role Overview

As a Lead Data Scientist / Data Analyst, you’ll combine analytical thinking, business acumen, and technical expertise to design and deliver impactful data-driven solutions. You’ll lead analytical problem-solving for retail clients — from data exploration and visualisation to predictive modelling and actionable business insights.

Key Responsibilities

• Partner with business stakeholders to understand problems and translate them into analytical solutions.
• Lead end-to-end analytics projects — from hypothesis framing and data wrangling to insight delivery and model implementation.
• Drive exploratory data analysis (EDA), identify patterns/trends, and derive meaningful business stories from data.
• Design and implement statistical and machine learning models (e.g., segmentation, propensity, CLTV, price/promo optimisation).
• Build and automate dashboards, KPI frameworks, and reports for ongoing business monitoring.
• Collaborate with data engineering and product teams to deploy solutions in production environments.
• Present complex analyses in a clear, business-oriented way, influencing decision-making across retail categories.
• Promote an agile, experiment-driven approach to analytics delivery.
  
  

Common Use Cases You’ll Work On

• Customer segmentation (RFM, mission-based, behavioural)
• Price and promo effectiveness
• Assortment and space optimisation
• CLTV and churn prediction
• Store performance analytics and benchmarking
• Campaign measurement and targeting
• Category in-depth reviews and presentation to the L1 leadership team
  
  

• 3+ years of experience in data science, analytics, or consulting (preferably in the retail domain)
• Proven ability to connect business questions to analytical solutions and communicate insights effectively
• Strong SQL skills for data manipulation and querying large datasets
• Advanced Python for statistical analysis, machine learning, and data processing
• Intermediate PySpark / Databricks skills for working with big data
• Comfortable with data visualisation tools (Power BI, Tableau, or similar)
• Knowledge of statistical techniques (Hypothesis testing, ANOVA, regression, A/B testing, etc.)
• Familiarity with agile project management tools (JIRA, Trello, etc.)
  
  

Good to Have

• Experience designing data pipelines or analytical workflows in cloud environments (Azure preferred)
• Strong understanding of retail KPIs (sales, margin, penetration, conversion, ATV, UPT, etc.)
• Prior exposure to Promotion or Pricing analytics
• Dashboard development or reporting automation expertise
  
  

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