Experience, Skills & Knowledge

What you bring (Qualifications): 

Education & Experience: 

• Master’s degree in data science, computer science, applied mathematics, statistics, or a related quantitative field. 

• 6-9 years of professional experience in advanced analytics and machine learning roles. 

• Experience in analyzing complex and ambiguous business problems and translating them into data science/AI 

solutions.  

• Strong expertise in Python, SQL, and experience with big data processing tools (Spark, Hadoop).  Hands-on 

experience with deep learning frameworks (TensorFlow, PyTorch, Keras). 

• Experience in machine learning, supervised and unsupervised learning: Natural Language Processing (NLP), 

classification algorithms, advanced data/text mining techniques, multi-modal model development, and deep 

neural network architecture. 

• Experience in statistical learning methodologies including but not limited to Predictive & Prescriptive Analytics, 

Web Analytics, Parametric and Non-parametric models, Regression, Time Series Forecasting, Market Basket 

Analysis, Dynamic/Causal Inference Modeling, Statistical Learning, Guided Decisions, Topic Modeling  

• Familiarity with Autonomous AI systems design, implementation of human-in-the-loop frameworks, and 

expertise in AI system monitoring, observability and governance practices. 

• Experience in end-to-end LLM implementation including custom embedding generation, vector database 

management, LLM gateway configuration, RAG-based agent development, strategic model selection, iterative 

prompt refinement, model fine-tuning for accuracy optimization, and implementation of robust monitoring and 

governance frameworks. Proficiency in modern orchestration frameworks such as LangChain or DSPy. 

• Experience in MLOps best practices, including production-grade model deployment at scale, and 

designing/implementing enterprise-level AI/ML governance structures. 

• Expert communication and collaboration skills with the ability to work effectively with internal teams in a cross

cultural and cross-functional environment. Ability to communicate conclusions to both tech and non-tech 

audiences 

Desired Skills: 

• Retail industry experience and an understanding of retail/ecommerce data landscapes are strong pluses. 

• Expertise in model interpretability techniques and ethical/responsible AI considerations. 

• Familiarity with DevOps/DataOps practices for CI/CD of machine learning pipelines. 

• Familiarity with Graph DB architecture. 

• Proficiency in cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).