Principal Solution Architect

Solution Design and Architecture:

Collaborate with stakeholders to understand technical requirements related to the optimization of pricing and promotions.
Design scalable and robust architecture leveraging AWS services for machine learning and data processing.
Incorporate customer-centric design principles into software development and optimization processes.

AWS Cloud Integration:

Architect solutions on AWS cloud platform, recommending appropriate use of services such as SageMaker, S3, Redshift, Glue, and any other relevant tools.
Ensure scalability, reliability, and cost effectiveness of the AWS infrastructure supporting our ML applications.

Data Management and Integration:

Design data pipelines for ingesting, transforming, and managing large volumes of structured and unstructured data from diverse sources.
Propose ETL processes and collaborate with tech teams to ensure data quality and consistency.

Technical Leadership and Collaboration:

Provide guidance and mentorship to cross-functional teams including data scientists, analysts, and engineers
Collaborate with product managers, business stakeholders, and tech teams to align technical solutions with business goals.
Design software modules for data science components that are scalable, maintainable, and robust.
Oversee the integration of the pricing and promotion optimization algorithms within existing systems and platforms.

Performance Optimization and Testing:

Optimize algorithms and models for performance, scalability, and cost-effectiveness.
Implement best practices for testing and quality assurance.
Conduct rigorous monitoring and validation to ensure accuracy and reliability of ML models and software applications.

Documentation and Training:

Document high-level and detailed architecture designs, technical specifications, and implementation plans.
Provide training and support to end-users and technical staff on the use and maintenance of the software solution.

Proof of Concept and Prototyping:

Lead proof of concept initiatives to evaluate new technologies and methodologies.
Develop prototypes and pilot projects to demonstrate feasibility and potential business impact.

Cross-Functional Collaboration:

Collaborate with tech teams to ensure seamless deployment and operation of ML models in production environments.

Thought Leadership and Continuous Improvement:

Stay informed about emerging trends and advancements in machine learning, cloud computing, and retail technology.
Continuously evaluate and propose enhancements to existing systems and processes to improve efficiency and effectiveness.

Change Management and Adoption:

Drive change management initiatives to facilitate the adoption of new technologies and processes related to pricing and promotions optimization.
Provide training and support to internal teams on the use and benefits of ML-driven solutions in retail operations.