• Model Development: Work in end-to-end analytical products, from exploration and prototyping to productizing and testing, helping to solve complex business problems across the full value chain.
• Data Pipeline Engineering: Develop and maintain data pipelines for collecting, processing, and analyzing large datasets, ensuring data quality and consistency.
• Model Deployment: Deploy machine learning models into production environments, ensuring they are scalable, reliable, and easy to maintain.
• Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, MLOps engineers, product managers, and business stakeholders, to understand business needs and translate them into machine learning solutions.
• Performance Monitoring: Continuously monitor the performance of deployed models and implement improvements to enhance their accuracy, efficiency, and scalability.
• Research & Innovation: Stay up-to-date with the latest developments in machine learning and AI, and apply new techniques to improve existing models and processes.
• Documentation: Maintain clear and comprehensive documentation of models, processes, and systems to facilitate knowledge sharing and collaboration.

• 3+ years of experience as a Machine Learning Engineer, Data Scientist, or similar role.
• Experience in the retail industry or e-commerce is highly desirable.

• Strong problem-solving abilities, solid background in algorithms and data structures.
• Strong proficiency in Python.
• Ability to work with machine learning tools (eg. scikit-learn, tensorflow, keras, pytorch, Spark MLlib).
• Experience with big data using Databricks, Snowflake, Apache Spark or Hadoop.
• Proficiency in SQL and experience with relational databases.
• System level architecture understanding including scaling, MLOps, model/data monitoring, and ensuring a deterministic pipeline.
• Familiarity with cloud platforms (e.g., Azure) and containerization technologies (e.g., Docker, Kubernetes).
• Experience with version control systems (e.g., Git) and collaborative development tools (e.g., JIRA, Confluence).

• Strong problem-solving skills with the ability to work independently and collaboratively in a fast-paced environment.
• Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• A proactive attitude and a passion for continuous learning and innovation.