Research Scientist

Responsibilities

• Design, develop, and refine machine learning models using Python and relevant ML frameworks.
• Analyze and process large datasets for model training, validation, and evaluation.
• Use MongoDB for efficient data storage, manipulation, querying, and retrieval.
• Develop and maintain data preprocessing and feature engineering pipelines.
• Evaluate model performance using appropriate metrics, benchmarking, and validation techniques.
• Perform hyperparameter tuning and iterative model optimization.
• Collaborate with cross-functional teams to identify opportunities for model and workflow improvements.
• Integrate data pipelines into training and inference workflows.
• Document experiments, methodologies, results, and technical decisions to ensure reproducibility.
• Provide actionable insights and recommendations based on machine learning and data-driven findings.

Required Qualifications

• Professional experience working as a Machine Learning Engineer, ML Developer, Data Scientist, or similar role.
• Strong proficiency in Python and machine learning development.
• Hands-on experience with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
• Practical experience using MongoDB for data management and retrieval.
• Strong understanding of machine learning algorithms, model evaluation, and data preprocessing.
• Experience with feature engineering, hyperparameter tuning, and model benchmarking.
• Strong analytical and problem-solving abilities.
• Excellent written communication and technical documentation skills.
• Ability to work independently and collaborate effectively in a remote environment.

Preferred Qualifications

• Experience deploying or operationalizing machine learning models in cloud or enterprise environments.
• Familiarity with ML pipelines, model serving, and inference workflows.
• Experience working with large-scale datasets and production ML systems.
• Knowledge of MLOps, model monitoring, or automated ML workflows.
• Experience contributing to AI training, model evaluation, or data-quality initiatives.
• Strong understanding of scalable data and machine learning architectures.
• Ability to adapt quickly to evolving technical requirements and project priorities.