Data Scientist

Data ScientistBased in Bentonville, AR (Onsite) for a Full-time position.AI/Client model deployment/implementation skills are must for Walmart. Expertise with classical Client algorithm like K-NN, LSH, logistic regression, linear regression, SVM, Random forest and clustering. Good understanding of Client & DL algorithms and frameworks (Scikit-learn,Spacy, Tensorflow/Keras/ PyTorch). Experience in deep learning Algorithms like MLP, CNN, RNN, LSTMs and GANs, Transformers and LLMs. Excellent programming skills in Python. Expertise in Google Cloud and operationalization of models using MLOPs. Proven experience in deploying real-time AI/Client models using Google Cloud Platform. Strong programming skills in Python and PySpark. Proficiency with SQL and relational databases, data warehouses, and BigQuery. Experience in scaling marketing-related AI/Client solutions such as cross/upsell, recommended systems, and category propensity. Experience in deploying and managing Large scale Machine Learning Models is a plus. Expertise with classical Client algorithm like K-NN, LSH, logistic regression, linear regression, SVM, Random forest and clustering. Good understanding of Client & DL algorithms and frameworks (Scikit-learn,Spacy, Tensorflow/Keras/ PyTorch). Experience in deep learning Algorithms like MLP, CNN, RNN, LSTMs and GANs, Transformers and LLMs. Excellent programming skills in Python. Expertise in Google Cloud and operationalization of models using MLOPs. Experience in scheduling jobs for automated training and inference of AI/Client models using airflow or any other workflow orchestration platform. Proficiency in collecting data from different data sources, data cleaning, preprocessing, and feature engineering. Understanding of regression, classification, and unsupervised Client algorithms. Experience in mentoring junior associates in scaling AI/Client models. Excellent problem-solving and analytical skills. Strong written and verbal communication skills, with the ability to present and explain complex concepts to both technical and non-technical audiences.