Data Scientist

Position - Data Scientist

Experience - Min 3 Years

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Notice Period:

Interview Process:

Interview Mode:

Interview Timeline:

Industry:

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.