Data Scientist

Job Title: Data Scientist – AWS / Azure / GCP Experience: 3–15 Years Work: Hybrid Model Job Summary We are looking for a highly skilled Data Scientist with hands-on experience in designing, developing, and deploying machine learning and advanced analytics solutions on AWS, Microsoft Azure, or Google Cloud Platform (GCP). The ideal candidate should have strong expertise in statistics, machine learning, data engineering concepts, Python programming, and cloud-based AI/ML services to solve complex business problems and deliver scalable data-driven solutions. Roles & Responsibilities - Develop, train, validate, and deploy Machine Learning and Deep Learning models for business use cases. - Analyze structured and unstructured datasets to derive actionable business insights. - Build predictive, classification, clustering, recommendation, and forecasting models. - Design scalable ML pipelines using cloud-native AI/ML services on AWS, Azure, or GCP. - Perform data cleaning, feature engineering, model selection, hyperparameter tuning, and model evaluation. - Work with large datasets using SQL, Python, Spark, or distributed computing frameworks. - Collaborate with business stakeholders, product teams, and data engineers to understand requirements and deliver AI-driven solutions. - Deploy and monitor ML models in production environments using MLOps best practices. - Implement model monitoring, drift detection, retraining, and performance optimization. - Build reusable data science components, APIs, and automation pipelines. - Develop dashboards and visualizations to communicate analytical findings. - Apply statistical analysis and experimentation techniques to solve business problems. - Optimize machine learning models for scalability, accuracy, and performance. - Work with cloud storage, data lakes, and data warehouses for enterprise-scale analytics. - Ensure data quality, governance, security, and compliance standards are followed. - Stay updated with emerging AI, Machine Learning, Generative AI, and cloud technologies. Required Technical Skills Programming - Python - SQL - R (Good to Have) Machine Learning - Supervised Learning - Unsupervised Learning - Classification - Regression - Clustering - Recommendation Systems - Time Series Forecasting - NLP - Deep Learning AI & Data Science Libraries - Pandas - NumPy - Scikit-learn - TensorFlow - PyTorch - Keras - XGBoost - LightGBM - CatBoost Data Engineering - ETL Concepts - Data Pipelines - Apache Spark - PySpark - Hadoop (Good to Have) Cloud Platforms (Any One) AWS - SageMaker - S3 - Glue - Lambda - Redshift - Athena - EMR Microsoft Azure - Azure Machine Learning - Azure Data Factory - Azure Synapse - Azure Databricks - Azure Data Lake Storage - Azure Functions Google Cloud Platform (GCP) - Vertex AI - BigQuery - Cloud Storage - Dataflow - Dataproc - Pub/Sub - Cloud Functions Databases - SQL Server - PostgreSQL - MySQL - Oracle - MongoDB - Snowflake (Good to Have) Visualization - Power BI - Tableau - Matplotlib - Seaborn - Plotly MLOps & DevOps - MLflow - Docker - Kubernetes - Git - Jenkins - CI/CD Pipelines - Model Deployment - Model Monitoring Big Data (Good to Have) - Spark - Kafka - Hive - Airflow Generative AI (Good to Have) - Large Language Models (LLMs) - LangChain - LangGraph - Vector Databases - RAG (Retrieval-Augmented Generation) - Prompt Engineering - OpenAI APIs - Azure OpenAI - Amazon Bedrock - Vertex AI Generative AI Soft Skills - Strong analytical and problem-solving abilities - Excellent communication and stakeholder management skills - Ability to work in Agile/Scrum environments - Strong collaboration and teamwork - Ability to translate business problems into analytical solutions - Continuous learning mindset Preferred Qualifications - Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related field. - Cloud certifications (AWS, Azure, or GCP) are an added advantage. - Experience working with enterprise-scale cloud data platforms. - Exposure to MLOps, AI governance, and production model deployment.