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.