
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 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

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