Data Scientist

JOB DESCRIPTION - DATA SCIENTIST

Company: Orlin Apparel Private Limited

Position: Data Scientist

Department: Data Science / Business Analytics / IT

Industry: Textile & Apparel / Fashion / E-commerce

Location: Surat, Gujarat

Employment Type: Full-Time

Experience: 25 Years

Reports To: Head IT / Business Head / Management

1. Job Summary

Orlin Apparel Private Limited is looking for a Data Scientist who can use data, statistical

analysis, Artificial Intelligence, and Machine Learning to solve business problems and

improve decision-making across fashion design, production, inventory, sales, e-commerce,

marketing, and supply chain operations.

The candidate will be responsible for collecting and analyzing business data, developing

predictive models, creating dashboards and insights, and implementing data-driven solutions

to improve sales, inventory planning, customer understanding, and operational efficiency.

2. Key Responsibilities

A. Data Collection & Analysis

• Collect, clean, validate, and analyze data from ERP, OMS, e-commerce platforms,

Excel, databases, and other business systems.

• Combine data from sales, inventory, production, procurement, marketing, warehouse,

and customer sources.

• Identify trends, patterns, anomalies, and business opportunities.

• Ensure data quality, consistency, and accuracy.

B. Sales & Demand Forecasting

• Develop models to forecast sales and product demand.

• Forecast demand at SKU, category, size, color, season, and channel level.

• Analyze historical sales, seasonality, promotions, pricing, and customer behavior.

• Support management in deciding which products and quantities should be produced

or stocked.

• Monitor forecast accuracy and continuously improve forecasting models.

Demand forecasting and product-level forecasting are particularly relevant in apparel because

data science can help determine collection, inventory, and channel decisions.

C. Inventory & Supply Chain Analytics

• Develop data-driven solutions for inventory optimization.

• Identify fast-moving, slow-moving, and non-moving products.

• Analyze stock-outs, excess inventory, sell-through, and inventory turnover.

• Support replenishment planning.

• Identify opportunities to reduce excess and obsolete stock.

• Develop predictive models for inventory requirements.

D. E-commerce Analytics

• Analyze sales data from platforms such as Amazon, Flipkart, website, and other

marketplaces used by the company.

• Analyze product performance and SKU-level sales.

• Monitor conversion rate, average order value, returns, cancellations, RTO, and

customer behavior.

• Identify high-performing and low-performing products.

• Develop models to predict product demand and customer purchase behavior.

• Provide data-driven recommendations for e-commerce growth.

E. Customer & Marketing Analytics

• Analyze customer purchasing behavior and preferences.

• Develop customer segmentation models.

• Identify repeat customers and potential high-value customers.

• Analyze campaign and advertisement performance.

• Support personalized product and promotion recommendations.

• Measure marketing ROI and customer acquisition performance.

F. Product & Fashion Analytics

• Analyze performance by:

• Product category
• Style
• Color
• Size
• Fabric
• Price range
• Collection
• Season

• Identify emerging product trends from historical and current sales data.

• Provide insights to Fashion Design, Merchandising, and Product Development teams.

• Support data-driven collection planning.

G. AI & Machine Learning

• Develop and implement ML models for business use cases.

• Apply appropriate techniques such as:

• Regression
• Classification
• Clustering
• Time-Series Forecasting
• Recommendation Systems
• Anomaly Detection
• Predictive Analytics

• Explore Generative AI/LLM applications where they can create measurable business

value.

• Automate repetitive analytical and reporting activities.

H. Dashboard & Business Reporting

• Develop dashboards and analytical reports using Power BI / Tableau / Python /

Excel.

• Create management dashboards for:

• Sales
• Inventory
• E-commerce
• Production
• Marketing
• Customer analytics
• Demand forecasting

• Convert complex data into simple and actionable business insights.

• Present findings to management and department heads.

I. Model Development & Deployment

• Define business problems and convert them into data science projects.

• Perform exploratory data analysis.

• Build, test, validate, and optimize models.

• Monitor model performance after deployment.

• Work with IT/Data Engineering teams to integrate models into business applications.

End-to-end ownership—from problem definition and data preparation through model

development, validation, deployment, and business implementation—is a standard

expectation for modern apparel/retail data-science roles.

3. Key Projects Expected at Orlin

The Data Scientist may work on projects such as:

1. SKU-wise Sales Forecasting

2. Demand Forecasting for New Collections

3. Inventory Optimization

4. Stock-Out Prediction

5. Slow-Moving Stock Prediction

6. E-commerce Sales Prediction

7. Customer Segmentation

8. Product Recommendation System

9. Return/RTO Prediction

10. Price & Promotion Analytics

11. Marketing Campaign Performance Prediction

12. Production Demand Planning

13. Sales Trend & Seasonality Analysis

14. Automated MIS & Management Dashboards

15. AI-based Business Process Automation

4. Required Technical Skills

Programming

• Python – Mandatory

• Pandas

• NumPy

• Scikit-learn

• Matplotlib / Seaborn

• Basic PyTorch or TensorFlow preferred

Database

• Strong SQL knowledge

• Data extraction and transformation

• Database management concepts

Machine Learning

• Regression

• Classification

• Clustering

• Decision Trees

• Random Forest

• XGBoost

• Time-Series Forecasting

• Model Evaluation

• Feature Engineering

Data Visualization

• Power BI / Tableau

• Advanced Excel

• Dashboard development

• Data storytelling

Advanced / Preferred Skills

• NLP

• Generative AI / LLM

• Recommendation Systems

• Deep Learning

• MLOps

• Cloud platforms such as AWS, Azure, or GCP

Current apparel-focused Data Scientist roles commonly emphasize Python, SQL, ML,

forecasting, optimization, and deployment, with GenAI/cloud/MLOps as additional skills.

5. Educational Qualification

• Bachelor's or Master's degree in:

• Data Science
• Statistics
• Mathematics
• Computer Science
• Artificial Intelligence
• Machine Learning
• Engineering
• Economics or a related quantitative field

Preferred: M.Sc. / MCA / M.Tech / MBA Analytics / PG in Data Science or equivalent.

6. Experience

2–5 years of relevant experience in Data Science, Machine Learning, Business Analytics,

E-commerce Analytics, Retail Analytics, or a related field.

Experience in Fashion, Apparel, Textile, Retail, E-commerce, or Manufacturing will be

an added advantage.

7. Key Competencies

• Strong analytical and problem-solving ability

• Business-oriented thinking

• Strong statistical understanding

• Good communication and presentation skills

• Ability to explain technical results to non-technical teams

• Attention to data accuracy and detail

• Ability to work independently and cross-functionally

• Strong curiosity and continuous-learning mindset

8. Key Departments for Coordination

The Data Scientist will work closely with:

• Management

• Sales

• E-commerce

• Marketing

• Fashion Design

• Merchandising

• Production

• Planning

• Inventory / Warehouse

• Procurement

• Finance / Accounts

• IT / Data Engineering