Solution Development: Engage in the ideation, prototyping, and implementation of new AI/ML solutions to meet emerging business requirements.
Machine Learning Pipelines: Design, develop, and implement end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment.
Deep Learning Models: Develop, train, and optimize deep learning models using neural network architectures (e.g., CNNs, RNNs, Transformers, GANs) and frameworks such as TensorFlow, Keras, or PyTorch. Apply these models to solve complex problems in areas like computer vision, natural language processing (NLP), and time-series forecasting.
Collaboration: Work closely with data architects, data engineers, and software developers to ensure effective data collection, processing, and storage. Collaborate with external partners, research institutions, and subject matter experts to gather domain-specific knowledge and datasets.
Exploratory Data Analysis: Perform exploratory data analysis to identify patterns, generate insights, and communicate findings to stakeholders.
Model Optimization: Optimize model performance by addressing issues such as overfitting, underfitting, and bias, ensuring scalability and performance in production environments.
Mentorship: Mentor junior data scientists in model development, data handling, and ethical considerations in AI/ML practices.
Continuous Learning: Stay abreast of the latest advancements in machine learning and AI research, applying new methods to enhance model performance and scalability.