Data Scientist

Description :

Job Title : Data Scientist

Experience : 5 Years

Location : Remote/ Indore/ Mumbai/ Chennai/ Gurugram

Industry : Must be from BPO/KPO or Healthcare Org or Shared Services

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 :

- Masters 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 , , 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