Data Scientist - II

• Advanced Data Analysis: Conduct in-depth analyses of large datasets from multiple sources, such as clickstream data, sales transactions, and user behavior, to uncover actionable insights.
• Machine Learning: Develop, implement, and maintain sophisticated machine learning models for use cases including recommendations, personalization, customer segmentation, demand forecasting, and price optimization.
• A/B Testing: Design and analyze experiments to evaluate the impact of new product features, marketing campaigns, and user experiences on business metrics.
• Data Engineering Collaboration: Work closely with data engineers to ensure robust, accurate, and scalable data pipelines for analysis and model deployment.
• Cross-functional Collaboration: Partner with product, marketing, and engineering teams to identify data needs, define analytical approaches, and deliver impactful insights.
• Dashboard Development: Create and maintain dashboards using modern visualization tools to present findings and track key performance metrics.
• Exploratory Data Analysis: Investigate trends, anomalies, and patterns in data to guide strategy and optimize performance across various business units.
• Optimization Strategies: Apply statistical and machine learning methods to optimize critical areas such as supply chain operations, customer acquisition, retention strategies, and pricing models.

• Programming: Proficiency in Python (preferred) or R for data analysis and machine learning.
• SQL Expertise: Advanced skills in querying and managing large datasets.
• Machine Learning Frameworks: Hands-on experience with tools like Scikit-learn, TensorFlow, or PyTorch.
• Data Processing: Strong expertise in data wrangling and transformation for model readiness.
• A/B Testing: Deep understanding of experimental design and statistical inference.
• Visualization: Experience with tools such as Tableau, Power BI, Matplotlib, or Seaborn to create insightful visualizations.
• Statistics: Strong foundation in probability, hypothesis testing, and predictive modeling techniques.
• Communication: Exceptional ability to translate technical findings into actionable business insights.

• Domain Knowledge: Prior experience with e-commerce datasets, including user behavior, transaction data, and inventory management.
• Big Data: Familiarity with Hadoop, Spark, or BigQuery for managing and analyzing massive datasets.
• Cloud Platforms: Proficiency with cloud platforms like AWS, Google Cloud Platform (GCP), or Azure for data storage, computation, and model deployment.
• Business Acumen: Understanding of critical e-commerce metrics such as conversion rates, customer lifetime value (LTV), and customer acquisition costs (CAC).

• Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related quantitative field. Advanced certifications in machine learning or data science are a plus.

Purplle