Lead the creation and deployment of machine learning models for demand forecasting, discovering opportunities to enhance supply chain efficiency. Design and develop data-intensive systems using Python and frameworks like Dask, focusing on performance and scalability. Develop forecasting models for insights into expected returns. This role involves designing, developing, and testing new algorithms and models based on machine learning or operation research, staying current with scientific libraries, and continuously improving code quality according to Blue Yonder standards. Participate in the operation of machine learning services and team activities.
Good To Have:
Participate in team activities such as backlog grooming, planning, daily standups, and retrospectives.
Demonstrate problem solving and innovation ability.
Integrate new models and algorithms into a product solution with limited assistance.
Translate business requirements to user stories and actively seek feedback by the stakeholders.
Timely and proactively communicate impediments to commitments.
Communicate information in the team and assist in external communication with customers and partners.
Act according to company and team visions and require user stories to adhere to those visions.
Write effective test cases.
Master some of Blue Yonder's domain expertise with knowledge acquired from past projects.
Actively provide feedback during code reviews.
Understand functional and technical requirements of software components.
Participate in translating scientific contributions into patents, publications, or conferences.
Autonomously pull issues from the team backlog or support other team members with their issues as appropriate.
Participate in team hiring activities.
Understand infrastructure costs of machine learning algorithms embedded in delivered products and services and make diligent use of provided resources.
Identify inefficient use of computational resources in the machine learning pipeline and use adequate patterns and technologies to resolve this.
Scale machine learning pipeline to meet throughput targets and time windows.
Must Have:
Design, develop, and test new algorithms, models, or solving approaches based on machine learning, operation research, or other techniques.
Stay current with scientific libraries and development tools.
Continuously improve themselves and the code they produce.
Develop quality software according to clean code principles and Blue Yonder standards.
Understand the problem domain the team works on.
Be perceived as the expert in a small area within the team and be the go-to person for related implementational and operational issues.
Participate in the operation of machine learning services through rolling out changes, resolving incidents, and fulfilling service requests.
Clearly communicate impediments and actively seek support by team members to overcome obstacles.
Add these skills to join the top 1% applicants for this job
data-analytics
forecasting-budgeting
game-texts
test-coverage
data-visualization
data-science
scikit-learn
pytorch
pandas
deep-learning
python
algorithms
sql
machine-learning
Scope:
Lead the creation and deployment of machine learning models for demand forecasting.
Discover opportunities to enhance supply chain efficiency by applying various data science techniques, analyzing customer data, and experimenting to solve real-world business challenges in retail planning.
Design and develop data-intensive systems using Python and frameworks like Dask, with an emphasis on performance and scalability.
Develop forecasting models to provide insights into expected returns, helping retailers adjust stocking levels, labor, and reordering strategies.
What you’ll do:
Primary Duties and Responsibilities
Consistently delivers solid quality and helps the team, in particular:
Is responsible for designing, developing, and testing new algorithms, models, or solving approaches based on machine learning, operation research, or other techniques to solve a Blue Yonder business problem under the supervision of a senior team member or manager.
Stays current with scientific libraries and development tools.
Continuously improves themselves and the code they produce.
Develops quality software according to clean code principles and Blue Yonder standards.
Has an understanding of the problem domain the team works on.
Is perceived as the expert in a small area within the team and is the go-to person for related implementational and operational issues.
Participates in the operation of machine learning services through rolling out changes, resolving incidents, and fulfilling service requests.
Clearly communicates impediments and actively seeks support by team members to overcome obstacles.
Secondary Duties and Responsibilities
Participates in team activities such as backlog grooming, planning, daily standups, and retrospectives.
Demonstrates problem solving and innovation ability.
Integrates new models and algorithms into a product solution with limited assistance.
Translates business requirements to user stories and actively seeks feedback by the stakeholders.
Timely and proactively communicates impediments to commitments.
Communicates information in the team.
Communicates information internally and assists in external communication with customers and partners.
Acts according to company and team visions and requires user stories to adhere to those visions.
Writes effective test cases.
Has mastered some of Blue Yonder's domain expertise with knowledge acquired from past projects.
Actively provides feedback during code reviews.
Understands functional and technical requirements of software components.
Participates in translating scientific contributions into patents, publications, or conferences.
Autonomously pulls issues from the team backlog or supports other team members with their issues as appropriate.
Participates in team hiring activities.
Understands infrastructure costs of machine learning algorithms embedded in delivered products and services and makes diligent use of provided resources.
Identifies inefficient use of computational resources in the machine learning pipeline and uses adequate patterns and technologies to resolve this.
Scales machine learning pipeline to meet throughput targets and time windows.
What we are looking for:
Machine Learning, Deep Learning, Statistical Analysis
Optimization: Markov Decision Process, dynamic programming, etc.