Senior Data Scientist

Advance Stores Company•Raleigh, NC

1d•Hybrid

About The Position

Job Summary: We are seeking a highly skilled and experienced Senior Data Scientist with deep expertise in machine learning model development and production deployment, particularly in predictive and prescriptive analytics. The ideal candidate will have a strong technical background in Python, PySpark, TensorFlow/PyTorch, advanced ML algorithms (XGBoost, LightGBM, structural time series), and experience building scalable AI/ML architectures using platforms like Databricks and cloud services. This role also requires strong collaboration and mentoring capabilities to translate ambiguous business challenges into end-to-end data science solutions, work effectively with cross-functional stakeholders, and guide junior team members through Agile development cycles. This position is 4 days in office, 1 day remote per week, based at our corporate headquarters in Raleigh, North Carolina (North Hills) Key Responsibilities Model Development & Technical Expertise Collaborate with cross-functional teams— IT Product, Merchandising, Supply Chain, and Finance—to Design and deploy machine learning solutions for demand forecasting, pricing, labor optimization, and supply chain efficiency. Hand-on expertise in training models such as structural time series modeling and boosting algorithms (e.g., XGBoost, LightGBM) using Gigabyte/petabyte scale data using pyspark or similar frameworks. Hands-on Expertise and knowledge of MLOps best practices and ML Engineering, to design and build robust batch and inference ML architectures. Establish best practices for model development, validation, and monitoring to ensure accuracy, scalability, and impact. Develop and tune machine learning models using Python, PySpark, TensorFlow, and PyTorch. Create robust and scalable AI/ML frameworks and build robust architectures. Familiarity of GenAI and agentic solutions, Retrieval-Augmented Generation (RAG) and model distillation is a plus. Collaboration & Communication Promote data-driven culture through analytical storytelling and stakeholder engagement. Work closely with stakeholders to understand business challenges and translate vague business requirements into data science solutions and work in the end-to-end solutioning. Collaborate with cross-functional teams to ensure successful integration of models into business processes. Ability to mentor and provide guidance to junior team members. Monitoring & Visualization Rapidly prototype and test hypotheses to validate model approaches. Build automated workflows for model monitoring and performance evaluation. Create dashboards using tools like Databricks and Palantir to visualize key model metrics like model drift, shapley values etc. Agile Execution Operate effectively in Agile environments, contributing to iterative development cycles and sprint planning. Adapt quickly to changing priorities while maintaining high standards of quality and innovation.

Requirements

5+ years of experience in Data Science, Machine Learning, AI/GenAI.
Master’s degree in computer science, Statistics, Operations Research or similar.
Hand-on expertise in training models such as structural time series modeling and boosting algorithms (e.g., XGBoost, LightGBM) using Gigabyte/petabyte scale data using pyspark or similar frameworks.
Hands-on Expertise and knowledge of MLOps best practices and ML Engineering, to design and build robust batch and inference ML architectures.
Develop and tune machine learning models using Python, PySpark, TensorFlow, and PyTorch.
Operate effectively in Agile environments, contributing to iterative development cycles and sprint planning.

Nice To Haves

Experience in Retail Industry
AI/ML certifications in Vertex AI/Databricks/AWS.
1+ years of experience as team lead.
Familiarity of GenAI and agentic solutions, Retrieval-Augmented Generation (RAG) and model distillation is a plus.

Responsibilities

Design and deploy machine learning solutions for demand forecasting, pricing, labor optimization, and supply chain efficiency.
Train models such as structural time series modeling and boosting algorithms (e.g., XGBoost, LightGBM) using Gigabyte/petabyte scale data using pyspark or similar frameworks.
Design and build robust batch and inference ML architectures.
Establish best practices for model development, validation, and monitoring to ensure accuracy, scalability, and impact.
Develop and tune machine learning models using Python, PySpark, TensorFlow, and PyTorch.
Create robust and scalable AI/ML frameworks and build robust architectures.
Promote data-driven culture through analytical storytelling and stakeholder engagement.
Work closely with stakeholders to understand business challenges and translate vague business requirements into data science solutions and work in the end-to-end solutioning.
Collaborate with cross-functional teams to ensure successful integration of models into business processes.
Mentor and provide guidance to junior team members.
Rapidly prototype and test hypotheses to validate model approaches.
Build automated workflows for model monitoring and performance evaluation.
Create dashboards using tools like Databricks and Palantir to visualize key model metrics like model drift, shapley values etc.
Operate effectively in Agile environments, contributing to iterative development cycles and sprint planning.
Adapt quickly to changing priorities while maintaining high standards of quality and innovation.