Business Analyst – Merchandising
Quess IT Staffing (Formerly known as Magna Infotech)- Posted 11 hours ago
- Be among the first 10 applicants
Job Description
We are looking for an experienced Business Analyst – Merchandising to drive data-led decision-making across retail merchandising, supply chain and pricing functions.
The role involves working closely with business stakeholders across Planning, Buying and Finance to develop analytical solutions that improve demand forecasting, inventory management, assortment planning and pricing strategies.
The ideal candidate should possess strong hands-on expertise in Python and SQL, along with experience in predictive analytics, statistical modelling and data visualization. Candidates should be able to translate complex business challenges into actionable analytical insights and measurable business outcomes.
Key Responsibilities
- Develop and implement analytical solutions across merchandising, supply chain and pricing functions.
- Work with business stakeholders to identify business challenges and translate them into analytical requirements.
- Build predictive models and rule-based algorithms for demand forecasting, pricing and discount optimization.
- Drive improvements in inventory management, assortment planning and product allocation.
- Apply statistical and machine learning techniques to solve complex retail business problems.
- Analyze large datasets and generate actionable business insights.
- Manage analytical projects end-to-end, ensuring quality and timely delivery.
- Develop dashboards and reports using Power BI, Tableau or QlikView.
- Present analytical findings and recommendations to senior management and business stakeholders.
- Mentor junior team members and contribute to analytics capability development.
Mandatory Skills
- Strong experience in Retail Analytics / Merchandising Analytics.
- Hands-on proficiency in Python and SQL.
- Experience in Predictive Analytics and Statistical Modelling.
- Strong understanding of retail merchandising concepts:
- Inventory Management
- Assortment Planning
- Demand Forecasting
- Pricing and Discount Optimization
- Location and Space Optimization
- Shelf Placement
- Knowledge of analytical techniques:
- Regression and Classification
- Clustering
- Time Series Analysis
- Optimization Techniques
- A/B Testing
- Tree-Based and Ensemble Models
- Experience with data visualization tools such as Power BI, Tableau or QlikView.
- Strong analytical, problem-solving and stakeholder management skills.
Good-to-Have Skills
- Exposure to PySpark.
- Knowledge of cloud platforms, preferably Microsoft Azure.
- Experience handling large datasets and advanced analytics solutions.
- Understanding of retail applications and business processes.
Educational Qualification
Bachelor's or Master's degree in Statistics, Mathematics, Economics, Operations Research, Engineering or another quantitative discipline.
Key Skills
Retail Analytics | Merchandising Analytics | Python | SQL | Predictive Analytics | Statistical Modelling | Demand Forecasting | Pricing Optimization | Inventory Management | Assortment Planning | Power BI | Tableau | PySpark
More Info
Key Skills
About Company
Quess Corp Limited (Quess) is India’s leading business services provider, leveraging our extensive domain knowledge and future-ready digital platforms to drive client productivity through outsourced solutions.
We provide a host of technology-enabled staffing and managed outsourcing services across processes such as sales & marketing, customer care, after-sales service, back office operations, telecom operations, manufacturing operations, facilities and security management, HR & F&A operations, IT & mobility services, etc.
Our passion for delivering exceptional services, augmented by proprietary digital platforms, has strongly established our credentials as India’s largest employer in the private sector and the biggest integrated business services provider in the country. With 550,000+ employees spread across 9 countries, we serve over 3,000 clients. We are proud to achieve this success as a 15-year old start-up.
