Data Scientist

• Design, develop, and validate machine learning and deep learning models from scratch.
• Own the complete modeling lifecycle, from data exploration and validation through deployment.
• Work with both structured and unstructured data, including documents, images, and other complex data types.
• Translate business challenges into effective machine learning solutions.
• Develop production-quality Python code for data processing pipelines and model implementation.
• Evaluate model performance and continuously improve solution quality.
• Collaborate closely with software engineers and cross-functional teams to deliver scalable AI solutions.

This role is ideal for someone with a strong theoretical foundation who also enjoys building practical, production-grade ML solutions., * Strong understanding of machine learning fundamentals and deep learning architectures.

• Experience designing and implementing neural networks, including CNNs and Transformer-based models.
• Solid knowledge of loss functions, activation functions, optimization techniques, and training strategies.
• Understanding of common deep learning challenges such as gradient flow, overfitting, and model optimization.
• Ability to implement classical algorithms and solve algorithmic problems using Python.

Modeling Experience

• Experience with supervised, unsupervised, and transfer learning.
• Practical experience with computer vision, including pretrained models and fine-tuning techniques.
• Experience working with embeddings and representation learning.
• Knowledge of time series modeling beyond standard library implementations.

Data Engineering

• Experience validating and assessing training data quality.
• Hands-on experience processing unstructured datasets such as PDFs, documents, and images.
• Experience working with large-scale datasets (100GB+) in cloud environments.

Engineering Skills

• Strong Python programming skills.
• Experience with Google Cloud Platform (GCP) and/or Microsoft Azure.
• Ability to write clean, maintainable, production-ready code.
• Familiarity with MLOps practices is considered an advantage.

Nice to Have

• Experience deploying machine learning models into production.
• Knowledge of modern MLOps tools and workflows.
• Experience optimizing large-scale training and inference pipelines.

• Opportunity to work on cutting-edge AI and machine learning projects.
• Challenging technical environment with experienced engineers.
• Professional growth through complex, high-impact projects.
• Competitive compensation and comprehensive benefits.
• Flexible working environment and supportive engineering culture.