Data Science Consultant - Remote

Data Science Consultant - Remote

About Blue Yonder

Blue Yonder is a leading SaaS and AI-driven Global Supply Chain Solutions software product company. Our solutions are used by:

• 81 of the global STORES Top 100 Retailers
• 79 of the global Consumer Goods Registry Top 100
• 20 of the Gartner Supply Chain Top 25

Blue Yonder employs over 5500 of the industry’s most experienced demand and supply chain experts globally to develop, deliver and support its solutions.

We continue to grow a team of the most advanced Supply Chain experts. Members of this team participate in on-site consulting and business software implementation projects for the largest and most advanced Retail, Manufacturing, and Distribution companies across Europe. Our consultants create the future standards of their core business processes.

We are seeking a highly skilled and motivated Data Science Consultant to join our Supply Chain Planning team. The ideal candidate will leverage data-driven insights to optimize supply chain processes, improve decision-making, and drive business results. You will work closely with cross-functional teams, utilizing advanced data analytics, machine learning, and optimization techniques to enhance supply chain efficiency, forecast demand, and solve complex operational challenges.

• Data Analysis & Modeling:
  Analyze large, complex data sets related to demand forecasting, supply planning to uncover actionable insights.
• Machine Learning:
  Apply statistical and machine learning techniques to forecast demand, including data ingestion, data visualization and insights, verifying the integrity of data used for analysis, feature engineering, configuring forecasting models, fine‑tune forecasting model.
• Present results in a clear manner to external customers.
• Supply Chain Optimization:
  Implement optimization algorithms to improve supply chain efficiency, reduce costs, and enhance service levels.
• Consulting & Advisory:
  Serve as a subject matter expert (SME) for supply chain analytics, advising stakeholders on best practices, tools, and strategies for effective supply chain management.
• Process Improvement:
  Identify inefficiencies within the supply chain and recommend data‑driven strategies for continuous improvement.

• Desirable to have a background in statistical analysis or forecasting.
• Desirable to have Retail supply and demand forecasting experience.
• Ensure high‑quality deliverables and best practices.
• Build relationships and support business development.
• Stay up‑to‑date on ML and Data Engineering trends to continue advancing in your role.

• Educational Background:
  Bachelor’s or Master’s degree in Data Science, Computer Science, Industrial Engineering, Operations Research, Supply Chain Management, or a related field.
• Experience:
  
  5+ years of experience in data science or Supply Chain Operations Analytics, or worked on forecasting projects leveraging statistical/ML models; experience with data visualization tools.
• Technical
  
  Skills:
  
  Proficiency in Python, R, SQL, and experience with supply chain management platforms/processes. Good applied statistics skills (distributions, statistical testing, regression, etc.).
• Machine Learning:
  Hands‑on experience with machine learning techniques/algorithms (e.g., regression, classification, clustering) and optimization models.
• Industry Knowledge:
  Familiarity with supply chain processes such as demand forecasting, inventory management, procurement, logistics, and distribution.
• Analytical Thinking:
  Strong problem‑solving skills with the ability to analyze complex data sets and translate insights into actionable recommendations.

• Interpersonal and communication skills, with a focus on influencing and inspiring.
• Excellent problem‑solving skills.
• Organized, self‑motivated, and able to manage multiple priorities independently.
• Passion for continuous learning and team development.
• Fluent in English (German or French is a plus).
• Eligible to live and work in the EU.
• Pragmatism: we won’t always have the perfect solution, but we focus on finding the most practical alternative.