About the job:

You ll be joining a multidisciplinary team of product managers, marketers, designers and engineers who research and innovate on latest technology to create awesome digital products that millions of people will experience every day.

You will be responsible for prototyping machine learning algorithms and architecting machine learning systems that improve customer experience, enable innovative user experiences, and increase store revenue.

Key Responsibilities:

• Research, prototype, and develop algorithms and solutions to solve problems across retail.
• Work with tabular customer and transaction data to produce insights and analysis.
• Stay up to date with emerging technologies and learn new techniques/libraries/frameworks.
• Build and deploy algorithms and statistical solutions into production ready systems.
• Deliver on time with a high bar on quality of research, innovation, and engineering.

Basic Qualifications:

• Strong knowledge of the fundamentals of statistics and machine learning theories.
• 2 years of experience developing ML systems in cloud environments (AWS/Azure), preferably Databricks.

2 years of hands-on experience with machine learning tools and libraries (scikit-learn, TensorFlow/Keras, PyTorch, Huggingface, AWS Sagemaker and/or Databricks etc.)

• 2 years of experience building data engineering pipelines and performing exploratory data analysis on large datasets.
• 1 years of experience building and deploying software/models to production.
• Bachelor s Degree or higher in Computer Science/Engineering/Math, or relevant experience.

Preferred Qualifications:

• PhD in Statistics, AI, or Machine Learning (or) MS in Statistics, AI, or Machine Learning with 2 years or more of industry experience.
• Experience working on Databricks platform for ML solutioning, deployments and data engineering is a plus.
• Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance.
• Published research or Patents in machine learning, system design or data engineering.
• Impact on the AI community through conference presentations, blog posts or open-source contributions.