What You Can Expect in a Data Scientist Role

Think of yourself as someone who will design the end-user access layer of Business Intelligence projects and perform the detailed design of requested reports. You will collect and analyze data that helps understand the core of the business, design aggregation strategies, and support scalable, usable front-end solutions.

As a Data Scientist, you will be responsible for gathering, processing, and analyzing large data sets to support data-driven decision-making across the organization. You will apply predictive analytics to forecast trends and prescriptive analytics to recommend actionable strategies. You will build and deploy models to solve complex business problems and improve operational efficiency. You will work closely with cross-functional teams to define and track KPIs, ensuring measurable impact. Proficiency in Python, R, SQL, and data visualization platforms (e.g., Tableau, Power BI) is essential.

Key Responsibilities

AI/ML Development & Research

• Design, develop, and deploy advanced machine learning and deep learning models for complex business problems
• Implement and optimize Large Language Models (LLMs) and Generative AI solutions
• Build agentic AI systems with autonomous decision-making capabilities
• Conduct research on emerging AI technologies and their practical applications
• Perform model evaluation, validation, and continuous improvement

Cloud Infrastructure & Full-Stack Development

• Architect and implement scalable cloud-native ML/AI solutions on AWS, Azure, or GCP
• Develop full-stack applications integrating AI models with modern web technologies
• Build and maintain ML pipelines using cloud services (SageMaker, ML Engine, etc.)
• Implement CI/CD pipelines for ML model deployment and monitoring
• Design and optimize cloud infrastructure for high-performance computing workloads

Data Engineering & Database Management

• Design and implement data pipelines for large-scale data processing
• Work with both SQL and NoSQL databases (PostgreSQL, MongoDB, Cassandra, etc.)
• Optimize database performance for ML workloads and real-time applications
• Implement data governance and quality assurance frameworks
• Handle streaming data processing and real-time analytics

Leadership & Collaboration

• Mentor junior data scientists and guide technical decision-making
• Collaborate with cross-functional teams including product, engineering, and business stakeholders
• Present findings and recommendations to technical and non-technical audiences
• Lead proof-of-concept projects and innovation initiatives

Required Qualifications

• 5+ years of hands-on experience in data science and machine learning
• 3+ years of experience with deep learning frameworks and neural networks
• 2+ years of experience with cloud platforms and full-stack development (BPO preferred, healthcare domain, BFSI)

Technical Skills – Core AI/ML

• Machine Learning: Scikit-learn, XGBoost, LightGBM, advanced ML algorithms
• Deep Learning: TensorFlow, PyTorch, Keras, CNN, RNN, LSTM, Transformers
• Large Language Models: GPT, BERT, T5, fine-tuning, prompt engineering
• Generative AI: Stable Diffusion, DALL-E, text-to-image, text generation
• Agentic AI: Multi-agent systems, reinforcement learning, autonomous agents (optional)

Technical Skills – Development & Infrastructure

• Programming: Python (expert), R, Java/Scala, JavaScript/TypeScript
• Cloud Platforms: AWS (SageMaker, EC2, S3, Lambda), Azure ML, or Google Cloud AI
• Databases: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, Cassandra, DynamoDB)
• Full-Stack Development: React/Vue.js, Node.js, FastAPI, Flask, Docker, Kubernetes (optional)
• MLOps: MLflow, Kubeflow, model versioning, A/B testing frameworks
• Big Data: Spark, Hadoop, Kafka, streaming data processing (optional)

Preferred Qualifications

• Experience with vector databases and embeddings (Pinecone, Weaviate, Chroma)
• Knowledge of LangChain, LlamaIndex, or similar LLM frameworks
• Experience with model compression and edge deployment
• Familiarity with distributed computing and parallel processing
• Experience with computer vision and NLP applications
• Knowledge of federated learning and privacy-preserving ML
• Experience with quantum machine learning
• Expertise in MLOps and production ML system design (optional)

Key Competencies

Technical Excellence

• Strong mathematical foundation in statistics, linear algebra, and optimization
• Ability to implement algorithms from research papers
• Experience with model interpretability and explainable AI
• Knowledge of ethical AI and bias detection/mitigation

Problem-Solving & Innovation

• Strong analytical and critical thinking skills
• Ability to translate business requirements into technical solutions
• Creative approach to solving complex, ambiguous problems
• Experience with rapid prototyping and experimentation

Communication & Leadership

• Excellent written and verbal communication skills
• Ability to explain complex technical concepts to diverse audiences
• Strong project management and organizational skills
• Experience mentoring and leading technical teams

Education / Certifications

• Master’s or PhD in Computer Science, Data Science, Statistics, Mathematics, or related field (Not mandatory)