Data Scientist

Job Summary

Key Responsibilities

• Design, develop, and deploy AI/ML models for real-world use cases (e.g., predictive analytics, NLP, computer vision, recommendation systems).
• Perform data collection, preprocessing, feature engineering, and model training using frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Build and maintain end-to-end ML pipelines — from data ingestion and model training to deployment and monitoring.
• Collaborate with engineering teams to integrate trained models into applications via APIs, microservices, or other serving mechanisms.
• Implement model evaluation, validation, and tuning for optimal accuracy, precision, and robustness.
• Deploy and manage models in production environments using MLOps practices (CI/CD, version control, model monitoring, retraining).
• Optimize performance of models and systems for speed, scalability, and cost-efficiency.
• Work with cloud-based AI/ML platforms (AWS Sagemaker, GCP Vertex AI, or Azure ML) for training and deployment.
• Document experiments, results, and best practices for reproducibility and knowledge sharing.
• Stay current with emerging trends and tools in AI/ML and apply them to improve existing processes.
  

Required Skills And Qualifications

• 3–5 years of experience as a Data Science Engineer / ML Engineer / AI Developer.
• Proven hands-on experience building and training ML/DL models end-to-end (data prep → model development → deployment).
• Strong proficiency in Python and ML libraries such as TensorFlow, PyTorch, Keras, or Scikit-learn.
• Experience with data manipulation tools (Pandas, NumPy) and data visualization (Matplotlib, Seaborn, Plotly).
• Solid understanding of machine learning algorithms (classification, regression, clustering, NLP, CV).
• Experience with MLOps tools (MLflow, DVC, Kubeflow, or Airflow for ML pipelines).
• Experience deploying models using REST APIs, Docker, Kubernetes, or cloud ML services.
• Good knowledge of SQL and NoSQL databases and data warehousing concepts.
• Exposure to big data frameworks (e.g., Spark, Hadoop) is a plus.
• Strong problem-solving and analytical thinking skills.
• Excellent communication skills — ability to explain technical results to business stakeholders.
  

Preferred Qualifications

• Hands-on experience with Deep Learning architectures (CNNs, RNNs, Transformers).
• Experience working on NLP or Computer Vision projects.
• Knowledge of feature stores, model registries, and continuous model monitoring.
• Familiarity with Git, CI/CD pipelines, and cloud infrastructure (AWS/GCP/Azure).
• Degree in Computer Science, Data Science, AI/ML, or related quantitative field.