S&C GN Data & AI - Retail L7

 About The Role  


Job Title - AI/Gen AI Retail Specialist Level 7 SnC GN Data & AI


Management Level:07 - Manager


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


Must have skills:

Strong understanding of retail industry

Excellent communication skills with the ability to translate complex technical concepts into actionable business insights.

Experience managing cross-functional teams and delivering AI solutions in a fast-paced environment.

Experience in handling large datasets from different sources (e.g., transactional data, customer data, social media data) and data preprocessing techniques.

Expertise in supervised and unsupervised learning algorithms

Proficiency in deep learning frameworks like TensorFlow or PyTorch

Ability to implement NLP techniques for chatbots, recommendation systems, sentiment analysis, and personalized search.

Good to have skills:

Familiarity with SQL, NoSQL databases, and big data technologies like Spark for handling large-scale data.

Familiarity with transformer-based models such as BERT, GPT, and T5 for understanding and generating human language.

Experience with Generative Pre-trained Transformers (GPT)

Experience with Data Visualization Tools

Experience:610 years of relevant experience in data science, machine learning, or AI, with at least 2+ years in a leadership or managerial role.


Educational Qualification:

Msc. Statistics, B.Tech. / M.Tech Computer Science, Econometrics, Data Science

Retail tech experience in e-commerce, fashion, grocery, or quick-commerce segments.

Knowledge of ethical AI practices and responsible AI governance.

Experience working with APIs of commercial LLMs (e.g., OpenAI, Cohere, Anthropic).

Job

Summary:

We are seeking a dynamic and experienced AI / GenAI Retail Specialist (Manager level) to lead the development and implementation of artificial intelligence solutions across our retail operations. This role requires a strong blend of technical expertise, domain knowledge in the retail sector, and leadership capabilities to drive impactful AI and GenAI initiatives. Your expertise in AI will drive the development of cutting-edge solutions that enhance retail performance across various domains, including merchandising, pricing, customer engagement, and product assortment.



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.

Supervise teams in building, validating, and deploying ML/DL models using frameworks such as TensorFlow, PyTorch, and Hugging Face.

Develop and oversee the implementation of GenAI-powered tools

Design, develop, and implement AI algorithms (machine learning, deep learning, etc.) and generative models (e.g., GPT, GANs) tailored to the retail sector.

Focus on the creation of AI models that can help in wide range of use cases across customer lifecycle, personalized customer experiences, optimize pricing, and product assortment.

Translate data insights into actionable strategies for marketing, merchandising, and customer experience teams. For example, identifying cross-selling opportunities or optimizing product assortments.

Work closely with cross-functional teams (e.g., product, marketing, business development) to understand the retail business challenges and translate them into AI-driven solutions.

Communicate the potential of AI solutions to both technical and non-technical stakeholders, ensuring alignment between technical capabilities and business objectives.

 Qualification 

Professional & Technical Skills:

Proven experience with supervised and unsupervised learning, deep learning, and generative AI models (e.g., LLMs, transformers).

Expertise in machine learning, natural language processing (NLP), and generative AI models.

Experience with platforms like TensorFlow, PyTorch, and OpenAI technologies.

Knowledge of AI ethics and responsible AI practices.

Hands-on experience with cloud platforms (AWS, GCP, Azure) and modern MLOps practices.