Data Analyst – Retail Analytics

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

Required Skills and Experience

• 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