About the job

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

Qualification

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: Bachelor’s degree in Computer Science, Engineering, or related field required

Data Scientist