S&C Global Network - AI - Retail - Consultant - Retail Specialized Data Scientist

Job Title - Retail Specialized Data Scientist – Level 9 – SnC GN Data & AI

Management Level: 09 - Consultant

Location: Bangalore / Gurgaon / Mumbai / Chennai / Pune / Hyderabad / Kolkata

Must have skills:

• A solid understanding of retail industry dynamics, including key performance indicators (KPIs) such as sales trends, customer segmentation, inventory turnover, and promotions.
• Strong ability to communicate complex data insights to non-technical stakeholders, including senior management, marketing, and operational teams.
• Meticulous in ensuring data quality, accuracy, and consistency when handling large, complex datasets.
• Gather and clean data from various retail sources, such as sales transactions, customer interactions, inventory management, website traffic, and marketing campaigns.
• Strong proficiency in Python for data manipulation, statistical analysis, and machine learning (libraries like Pandas, NumPy, Scikit-learn).
• Expertise in supervised and unsupervised learning algorithms
• Use advanced analytics to optimize pricing strategies based on market demand, competitor pricing, and customer price sensitivity.

Good to have skills:

• Familiarity with big data processing platforms like Apache Spark, Hadoop, or cloud-based platforms such as AWS or Google Cloud for large-scale data processing.
• Experience with ETL (Extract, Transform, Load) processes and tools like Apache Airflow to automate data workflows.
• Familiarity with designing scalable and efficient data pipelines and architecture.
• Experience with tools like Tableau, Power BI, Matplotlib, and Seaborn to create meaningful visualizations that present data insights clearly.

Experience: Minimum 3 year(s) of experience is required

Educational Qualification: Bachelor’s or Master's degree in Data Science, Statistics, Computer Science, Mathematics, or a related field.

Job Summary:
The Retail Specialized Data Scientist will play a pivotal role in utilizing advanced analytics, machine learning, and statistical modeling techniques to help our retail business make data-driven decisions. This individual will work closely with teams across marketing, product management, supply chain, and customer insights to drive business strategies and innovations. The ideal candidate should have experience in retail analytics and the ability to translate data into actionable insights.

Roles & Responsibilities:

• Leverage Retail Knowledge: Utilize your deep understanding of the retail industry (merchandising, customer behavior, product lifecycle) to design AI solutions that address critical retail business needs.
• Gather and clean data from various retail sources, such as sales transactions, customer interactions, inventory management, website traffic, and marketing campaigns.
• Apply machine learning algorithms, such as classification, clustering, regression, and deep learning, to enhance predictive models.
• Use AI-driven techniques for personalization, demand forecasting, and fraud detection.
• Use advanced statistical methods help optimize existing use cases and build new products to serve new challenges and use cases.
• Stay updated on the latest trends in data science and retail technology.
• Collaborate with executives, product managers, and marketing teams to translate insights into business actions.

Professional & Technical Skills:

• Strong analytical and statistical skills.
• Expertise in machine learning and AI.
• Experience with retail-specific datasets and KPIs.
• Proficiency in data visualization and reporting tools.
• Ability to work with large datasets and complex data structures.
• Strong communication skills to interact with both technical and non-technical stakeholders.
• A solid understanding of the retail business and consumer behavior.
• Programming Languages: Python, R, SQL, Scala
• Data Analysis Tools: Pandas, NumPy, Scikit-learn, TensorFlow, Keras
• Visualization Tools: Tableau, Power BI, Matplotlib, Seaborn
• Big Data Technologies: Hadoop, Spark, AWS, Google Cloud
• Databases: SQL, NoSQL (MongoDB, Cassandra)