Develop and maintain machine learning pipelines for data processing, model training, and deployment.
Train, evaluate, and optimize machine learning models to ensure accuracy, efficiency, and scalability.
Monitor and evaluate the performance of machine learning models in production and identify opportunities for improvement.
Work with stakeholders, data analysts and data engineers to design, develop, and deploy machine learning models and deep learning architectures to solve business outcomes of customer acquisition, cross-sell/upsell, and customer retention.
Leverage languages such as Python and SQL, and relevant libraries (NumPy, Pandas) to manipulate data and draw insights from large data sets.
Build RAG pipelines to drive business outcomes by leveraging LLM techniques for facilitating improved customer engagement and decision-making processes.
This role requires
BS/MS degree in Computer Science or PhD in Statistics, Mathematics, Data Science or relevant field.
At least 3+ years of experience as a Data Scientist or ML Engineer.
Hands-on experience in writing production code in Python and SQL.
3+ years of experience in demonstrating use of numerical and machine learning libraries (NumPy, Pandas, scikit-learn, Tensorflow, etc.)
Strong experience with the open-source and AWS Cloud ecosystem (experience with EMR, EKS, Snowflake, S3, SageMaker, and abacus.ai).
Proven experience in designing, developing, and deploying machine learning models, with a strong focus on NLP tasks.
Expertise in LLM architectures and frameworks (e.g., Transformer models, GPT).
Expertise in Machine Learning/statistical techniques, including Regression, Classification,
Ensemble Methods, Deep Learning, and Reinforcement Learning).