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About the role
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 visualization to predictive modeling and actionable business insights. 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 optimization). 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. You will work on various common use cases including: - Customer segmentation (RFM, mission-based, behavioral) - Price and promo effectiveness - Assortment and space optimization - CLTV and churn prediction - Store performance analytics and benchmarking - Campaign measurement and targeting - Category in-depth reviews and presentation to the L1 leadership team Qualifications and Experience required: 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 visualization 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 (Note: No additional details of the company were provided in the job description) 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 visualization to predictive modeling and actionable business insights. 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 optimization). 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. You will work on various common use cases including: - Customer segmentation (RFM, mission-based, behavioral) - Price and promo effectiveness - Assortment and space optimization - CLTV and churn prediction - Store performance analytics and benchmarking - Campaign measurement and targeting - Category in-depth reviews and presentation to the L1 leadership team Qualifications and Experience required: 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 visualization 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 an