Senior Data Scientist

Walmart•Sunnyvale, CA

6d•Onsite

About The Position

The Senior Data Scientist develops custom data models and algorithms to support business solutions for Walmart eCommerce. The role builds large, complex datasets from internal and external sources; conducts advanced statistical analysis; develops learning systems for continuous data flows and offline analysis; trains, validates, and scales algorithms; and combines data features to develop search models. The position also researches industry techniques and technologies, models current-state processes to identify improvements, and works with project teams and cross-functional business partners to provide analytical support. The role uses a broad range of data science, machine learning, AI, computer vision, NLP, information retrieval, distributed processing, cloud, and software development technologies. Work includes pre-training and post-training AI models, human alignment, large-scale data engineering, model evaluation, and web crawling and ETL. The position is based at 1375 Crossman Avenue, Sunnyvale, California.

Requirements

Master’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, Data Analytics, or a related field and 1 year of experience in an analytics-related field; OR a bachelor’s degree in one of those fields and 3 years of experience in an analytics-related field.
Experience in scalable data analytics, statistical modeling, data visualization, and feature engineering using tools such as Power BI, Tableau, Pandas, NumPy, SciPy, Spark, and PySpark.
Experience with AI model pre-training, distributed training pipelines, large-scale data curation, and foundational architectures using PyTorch, DeepSpeed (ZeRO), and Hugging Face.
Experience with AI model post-training and human alignment, including supervised fine-tuning (SFT), RLHF, and transfer learning.
Experience with deep learning and computer vision, including image classification and feature extraction using EfficientNet, ResNet, and TensorFlow.
Experience with distributed data processing and large-scale data engineering in SQL/NoSQL environments, including query optimization in Hive, BigQuery, and Spark.
Experience with NLP text classification and sentiment analysis using BERT, RoBERTa, and Transformer architectures.
Experience with information retrieval using vector embeddings, cosine similarity, and FAISS for high-dimensional semantic search.
Experience with computer vision techniques including YOLO or Mask R-CNN for object detection, instance segmentation, and image enhancement.
Experience with cloud-based model training and inference using Azure ML Studio and GCP Vertex AI.
Experience with Git, GitHub, and CI/CD pipelines for version control and collaborative SDLC management.
Experience evaluating model performance using F1-score, ROC-AUC, and mAP, and conducting A/B testing.
Experience with web crawling and ETL using BeautifulSoup or Scrapy to collect and structure internal and external catalog data.
Employer will accept any amount of graduate coursework, graduate research experience, or professional experience with the required skills.

Responsibilities

Develop custom data models and algorithms to drive business solutions.
Build complex datasets from multiple internal and external data sources.
Conduct advanced statistical analysis to identify trends and significant relationships in data.
Build learning systems to analyze and filter continuous data flows and support offline data analysis.
Train algorithms to apply models to new datasets and future data.
Validate models and algorithmic techniques and scale algorithms to large datasets.
Combine data features to develop search models and provide appropriate search results.
Analyze large datasets and build algorithms to predict future data characteristics.
Research industry trends, new techniques, best practices, and up-to-date technologies.
Provide analytical support to Walmart eCommerce projects as part of project teams.
Work with cross-functional partners across the business.
Develop models of the current state to determine needed improvements.
Design distributed AI training pipelines, curate large-scale data, and optimize foundational architectures.
Implement supervised fine-tuning, reinforcement learning from human feedback, and transfer learning for domain-specific optimization.
Build and optimize deep learning and computer vision pipelines, including image classification, feature extraction, object detection, instance segmentation, and image enhancement.
Perform distributed data processing and large-scale data engineering in SQL and NoSQL environments, including query optimization and data lake management.
Develop NLP applications such as text classification and sentiment analysis.
Implement information retrieval and semantic search using vector embeddings, cosine similarity, and FAISS.
Use cloud services for model training, inference, and lifecycle management.
Manage project version control and collaborative software development workflows, including CI/CD pipelines.
Evaluate model performance using statistical metrics and A/B testing.
Use web crawling and ETL techniques to collect and structure data from internal and external catalogs.