Senior Data Scientist

Experience:

• Masters degree in data science, computer science, applied mathematics, statistics, or a related quantitative field.
• 5-9 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 crosscultural 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)