Data Scientist - hybrid - W2 only

Position Overview

As a Data Scientist ML/AI, you will play a pivotal role in developing, and deploying ML/AI models to revolutionize our retail operations. You will work with cross-functional teams to create data-driven solutions that enhance product recommendations, optimize inventory management, and personalize customer interactions.

Role Responsibilities

• Model Development: Develop, and implement ML/AI models and algorithms to address specific retail challenges, such as personalized recommendations, automated content generation, and predictive analytics.
• Data Engineering: Collaborate with data engineers to preprocess, clean, and structure data for model training and evaluation. Ensure data pipelines are optimized for efficiency and accuracy.
• Machine Learning Operations (MLOps):
• Deploy and maintain machine learning models in production environments (Astronomer, AWS, Snowpark). Implement MLOps practices to ensure scalability, robustness, and monitoring of deployed models.
• Collaboration: Work closely with data scientists, software engineers, and business stakeholders to understand requirements and translate them into technical solutions.
• Performance Optimization: Continuously evaluate and fine-tune models to improve performance and accuracy. Utilize feedback and metrics to enhance model effectiveness.
• Documentation and Reporting: Maintain thorough documentation of model development processes and results. Communicate findings and insights to both technical and non-technical stakeholders.
  
  

Key Qualifications

• Education: Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field. Advanced degree is a plus.
• Technical Skills:
• Proficiency in Python, including libraries such as TensorFlow, PyTorch, and scikit-learn.
• Experience with Large language models llama, Claude, etc.
• Knowledge of cloud platforms (e.g., AWS, GCP, Azure) and their machine learning services.
• Strong SQL knowledge for data manipulation and querying.
• Worked with modern data warehouses like Snowflake or BigQuery
• Used source control, Git and/or GitHub, proficiently
• Familiarity with MLOps tools and practices for deploying and managing machine learning models.
• Experience with data engineering practices and tools (e.g., ETL processes, data warehousing).
• Worked in an agile team environment
• Problem-Solving: Strong analytical and problem-solving skills with the ability to think creatively and strategically.
• Communication: Excellent verbal and written communication skills, with the ability to present complex technical concepts to diverse audiences.