Data Scientist

Role Overview: Data Scientist Location: Remote/ Indore/ Mumbai/ Chennai/ Gurugram Experience: Min 5 Years Work Mode: Remote Notice Period: Max. 30 Days (45 for Notice Serving) Interview Process: 2 Rounds Interview Mode: Virtual Face-to-Face Interview Timeline: 1 Week Industry: Must be from a BPO/ KPO/ Shared Services or Healthcare Org. Key Responsibilities: AI/ML Development & Research Design, develop, and deploy advanced machine learning and deep learning models to solve complex business problems. Implement and optimize Large Language Models (LLMs) and Generative AI solutions for real-world applications. Build agent-based AI systems with autonomous decision-making capabilities. Conduct cutting-edge research on emerging AI technologies and explore their practical applications. Perform model evaluation, validation, and continuous optimization to ensure high performance. Cloud Infrastructure & Full-Stack Development: Architect and implement scalable, cloud-native ML/AI solutions using AWS, Azure, or GCP. Develop full-stack applications that seamlessly integrate AI models with modern web technologies. Build and maintain robust ML pipelines using cloud services (e.G., SageMaker, ML Engine). Implement CI/CD pipelines to streamline ML model deployment and monitoring processes. Design and optimize cloud infrastructure to support high-performance computing workloads. Data Engineering & Database Management Design and implement data pipelines to enable large-scale data processing and real-time analytics. Work with both SQL and NoSQL databases (e.G., PostgreSQL, MongoDB, Cassandra) to manage structured and unstructured data. Optimize database performance to support machine learning workloads and real-time applications. Implement robust data governance frameworks and ensure data quality assurance practices. Manage and process streaming data to enable real-time decision-making. Leadership & Collaboration Mentor junior data scientists and assist in technical decision-making to drive innovation. Collaborate with cross-functional teams, including product, engineering, and business stakeholders, to develop solutions that align with organizational goals. Present findings and insights to both technical and non-technical audiences in a clear and actionable manner. Lead proof-of-concept projects and innovation initiatives to push the boundaries of AI/ML applications. Required Qualifications: Education & Experience Master’s or PhD in Computer Science, Data Science, Statistics, Mathematics, or a related field. 5+ years of hands-on experience in data science and machine learning, with a focus on real-world applications. 3+ years of experience working with deep learning frameworks and neural networks. 2+ years of experience with cloud platforms and full-stack development. Technical Skills - Core AI/ML Machine Learning: Proficient in Scikit-learn, XGBoost, LightGBM, and advanced ML algorithms. Deep Learning: Expertise in TensorFlow, PyTorch, Keras, CNNs, RNNs, LSTMs, and Transformers. Large Language Models: Experience with GPT, BERT, T5, fine-tuning, and prompt engineering. Generative AI: Hands-on experience with Stable Diffusion, DALL-E, text-to-image, and text generation models. Agentic AI: Knowledge of multi-agent systems, reinforcement learning, and autonomous agents. Technical Skills - Development & Infrastructure Programming: Expertise in Python, with proficiency in R, Java/Scala, JavaScript/TypeScript. Cloud Platforms: Proficient with AWS (SageMaker, EC2, S3, Lambda), Azure ML, or Google Cloud AI. Databases: Proficiency with SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra, DynamoDB). Full-Stack Development: Experience with React/Vue.Js, Node.Js, FastAPI, Flask, Docker, Kubernetes. MLOps: Experience with MLflow, Kubeflow, model versioning, and A/B testing frameworks. Big Data: Expertise in Spark, Hadoop, Kafka, and streaming data processing. Non Negotiables: Cloud Infrastructure - ML/AI solutions on AWS, Azure, or GCP Build and maintain ML pipelines using cloud services (SageMaker, ML Engine, etc.) Implement CI/CD pipelines for ML model deployment and monitoring Work with both SQL and NoSQL databases (PostgreSQL, MongoDB, Cassandra, etc.) Machine Learning: Scikit-learn Deep Learning: TensorFlow Programming: Python (expert), R, Java/Scala, JavaScript/TypeScript Cloud Platforms: AWS (SageMaker, EC2, S3, Lambda) Vector databases and embeddings (Pinecone, Weaviate, Chroma) Knowledge of LangChain, LlamaIndex, or similar LLM frameworks. Industry: Must be a BPO or Healthcare Org.