Data Scientist

Overview

We are seeking a Data Science Engineer with 3–5 years of experience in designing, developing, and deploying AI/ML solutions. The ideal candidate will have strong programming skills, hands-on experience in model development and training, and the ability to build scalable data and ML pipelines for production environments.

You’ll work closely with data scientists, software engineers, and product teams to develop machine learning models, train and fine-tune AI systems, and integrate them into real-world applications.

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.
  
  

• 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.
  
  

• 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.