Requirements
Requires 5 years of experience in data science, machine learning, or AI, with expertise in supervised/unsupervised learning, deep learning, NLP, computer vision, or generative AI. Must have strong proficiency in Python and/or R, familiarity with SQL, and ability to build data pipelines using Spark, Airflow, or Hadoop. Knowledge of model serving, API development, Docker, Kubernetes, CI/CD, MLOps tools, and deployment on cloud platforms (AWS, Google Cloud, Azure) is essential.
Description
Detailed Job Description : Data ScientistROLE PROFILE • Designs and develops scalable machine learning models and AI-driven solutions to address complex business challenges and enhance decision-making processesKEY RESPONSIBILITIES• Work with large and complex data sets to solve challenging business problems• Collect, clean, and preprocess large datasets for analysis & model training• Perform exploratory data analysis (EDA) to uncover insights and inform model development• Develop, train, and optimize machine learning models using state-of-the-art algorithms and frameworks• Build end-to-end ML pipelines , including data ingestion, transformation, model training, validation, and deployment• Automate workflows for model training, testing, and deployment using CI/CD pipelines and MLOps tools• Collaborate with cross-functional teams to integrate models into applications and deliver end-to-end solutions PROFESSIONAL EXPERIENCE/QUALIFICATIONS • 5 years of experience in data science, machine learning, or AI• Expertise in supervised/unsupervised learning , deep learning, NLP, computer vision, or generative AI (e.g., LLMs).• Strong proficiency in Python and/or R ; familiarity with SQL for data querying• Ability to build data pipelines (Spark, Airflow, Hadoop) and work with big data tools• Understanding of model serving , API development (FastAPI, Flask), and optimizing model performance for real-time or batch inference.• Knowledge of Docker, Kubernetes, CI/CD pipelines , and tools like MLflow/Kubeflow for model lifecycle management (MLOps)• Experience deploying models on AWS, Google Cloud, Azure, or similar (e.g., Sagemaker, Vertex AI)• Educational qualifications: Master in Computer Science or a related field.