Lead Data Scientist

Role & responsibilities

Key Responsibilities 

• Design, develop, and deploy ML models using classical algorithms (e.g., regression, decision trees, ensemble methods) and deep learning architectures (CNNs, RNNs, Transformers). 
• Build NLP solutions for tasks such as text classification, entity recognition, summarization, and conversational AI. 
• Develop and fine-tune GenAI models for use cases like content generation, code synthesis, and personalization. 
• Architect and implement Retrieval-Augmented Generation (RAG) systems for enhanced contextual AI applications. 
• Collaborate with data engineers to build scalable data pipelines and feature stores. 
• Perform advanced feature engineering and selection to improve model accuracy and robustness. 
• Work with large-scale structured and unstructured datasets using distributed computing frameworks. 
• Translate business problems into data science solutions and communicate findings to stakeholders. 
• Present insights and recommendations through compelling storytelling and visualization. 
• Mentor junior data scientists and contribute to internal knowledge sharing and innovation. 

Preferred candidate profile

• 10+ years of experience in data science, machine learning, and AI. 
• Strong academic background in Computer Science, Statistics, Mathematics, or related field (Masters or PhD preferred). 
• Proficiency in Python, SQL, and ML libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face). 
• Experience with NLP and GenAI tools (e.g., Azure AI Foundry, Azure AI studio, GPT, LLaMA, LangChain). 
• Hands-on experience with Retrieval-Augmented Generation (RAG) systems and vector databases. 
• Familiarity with cloud platforms (Azure preferred, AWS/GCP acceptable) and MLOps tools (MLflow, Airflow, Kubeflow). 
• Solid understanding of data structures, algorithms, and software engineering principles. 
• Experience with Aure, Azure Copilot Studio, Azure Cognitive Services 
• Experience with Azure AI Foundry would be a strong added advantage 

Preferred Skills 

• Exposure to LLM fine-tuning, prompt engineering, and GenAI safety frameworks. 
• Experience in domains such as consulting, finance, healthcare, retail, or enterprise SaaS. 
• Contributions to open-source projects, publications, or patents in AI/ML. 

Soft Skills 

• Strong analytical and problem-solving skills. 
• Excellent communication and stakeholder engagement abilities. 
• Ability to work independently and collaboratively in cross-functional teams. 
• Passion for continuous learning and innovation.