Assistant Manager / Manager - Data Scientist

Qualifications and Skills :

• Bachelor’s or Master’s degree in computer science, Data Science, or related quantitative field.
• 5–6 years of hands-on experience in data science and machine learning, Deep Learning and Natural Language Processing roles.
• Expertise in Python, SQL, and libraries like scikit-learn, XGBoost, TensorFlow, PyTorch, Transformers, LangChain, etc.
• Deep experience in feature engineering, data preprocessing, and exploratory data analysis.
• Solid understanding of model evaluation, bias-variance tradeoffs, and performance metrics (AUC, F1, precision-recall, etc.).
• Experience working with large datasets and cloud-based architectures (e.g., S3, BigQuery, Azure Blob).
• Proven track record in building and deploying GenAI applications or prototypes using tools like OpenAI APIs, HuggingFace models, or vector search engines (e.g., FAISS, Pinecone).
• Strong communication skills with the ability to translate technical insights to business stakeholders
• MLOps knowledge (e.g., MLflow, SageMaker, Vertex AI).
• Experience with CI/CD pipelines and containerization (Docker, Kubernetes).
• Contributions to open-source projects or personal AI/ML portfolio on GitHub or similar platforms.
• Domain experience in [e.g., financial services, healthcare, retail, Insurance] is a plus

Experience :

• 5–6 years of hands-on experience in data science and machine learning, Deep Learning and Natural Language Processing roles.

Roles and Responsibility:-

• Lead and drive end-to-end development of machine learning and AI models — from data ingestion and exploration to model evaluation, deployment, and monitoring.
• Design and implement advanced feature engineering pipelines that enhance model performance.
• Apply hyperparameter tuning techniques (e.g., grid search, Bayesian optimization) to improve model generalizability.
• Leverage state-of-the-art algorithms like XGBoost, LightGBM, CatBoost, and ensemble methods for complex business problems.
• Build and fine-tune LLMs and GenAI solutions (e.g., RAG pipelines, prompt engineering, embeddings, vector databases) for business use cases like summarization, chatbots, document intelligence, etc.
• Collaborate with cross-functional teams including product managers, domain experts, and data engineers to deliver business-aligned solutions.
• Develop reusable code modules and maintain high code quality using best practices (modularization, testing, version control).
• Work with cloud platforms (AWS, GCP, Azure) for model deployment, scalability, and MLOps integration.
• Guide junior team members and lead technical workstreams independently.