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Vice President of Data Science

3 months ago

Calgary, Alberta, Canada Blend360 Full time
Job Overview

At Blend360, we are committed to providing our clients with the essential data, insights, and innovations necessary to enhance their Supply Chain Strategy. We are in search of a Vice President of Data Science who can elevate the discipline within the Supply Chain Organization and deliver impactful solutions aimed at addressing our clients' most significant challenges.

Key Responsibilities

As a leader in Supply Chain Data Science, your role will encompass:

  1. Developing in-depth knowledge of supply chain dynamics by collaborating closely with stakeholders to identify critical business challenges.
  2. Implementing Data Science methodologies to tackle these challenges effectively.
  3. Enhancing the capabilities of Data Science within the supply chain sector, focusing on Artificial Intelligence (AI) and Machine Learning (ML).
  4. Promoting the benefits of Data Science to our clients concerning global supply chain operations.
Core Duties
  1. Fostering the growth of Data Science capabilities within the supply chain domain.
  2. Acquiring specialized knowledge in supply chain processes.
  3. Providing ethical leadership that inspires and develops the skills of direct reports while achieving organizational objectives.
  4. Overseeing analyst project work and overall team performance, including effective career development; managing challenging conversations and delivering constructive feedback with senior management support.
  5. Recruiting, onboarding, and training new team members.
  6. Evaluating team performance metrics, formulating solutions, and exploring alternative strategies to meet objectives.
  7. Building strong, productive relationships with clients to drive business growth.
  8. Understanding client requirements and tailoring existing business processes accordingly.
  9. Addressing client inquiries promptly and managing requests with professionalism.
  10. Collaborating strategically with leadership teams to align with client needs.
  11. Engaging with practice leaders and clients to comprehend business issues, industry context, data sources, potential risks, and constraints.
  12. Collaborating with practice leaders to translate business challenges into actionable Data Science solutions; presenting various approaches along with their advantages and disadvantages.
  13. Working with practice leaders to gather stakeholder feedback, achieve alignment on strategies, deliverables, and timelines.
  14. Creating a project roadmap that includes milestones, deadlines, responsibilities, risks, and contingency plans.
  15. Designing and maintaining efficient data pipelines, often integrated within client architectures.
  16. Assembling extensive, intricate data sets from both client and external sources that fulfill functional business requirements.
  17. Developing analytical tools to provide actionable insights into customer acquisition, operational efficiency, and other critical business performance indicators.
  18. Conducting data cleansing, quality control, and integrating data from both internal and external sources on an Advanced Data Science Platform.
  19. Summarizing and elucidating data and associated issues.
  20. Performing statistical data analysis, including exploratory data analysis and data mining, while documenting key insights and findings to inform decision-making.
  21. Training, validating, and cross-validating predictive models and machine learning algorithms using cutting-edge Data Science techniques and tools.
  22. Documenting predictive models and machine learning outcomes for inclusion in client-facing documentation.
  23. Assisting clients in deploying models and algorithms within their own systems.
Qualifications
  1. Master's degree in Statistics, Mathematics, Data Analytics, or a related quantitative discipline.
  2. A minimum of 5 years of professional experience in Advanced Supply Chain Data Science.
  3. Proficiency in one or more Advanced Data Science programming languages (R, Python, Scala, SAS).
  4. Demonstrated ability to transition machine learning models from research environments (e.g., Jupyter Notebooks) to production through procedural or pipeline methods.
  5. Experience with SQL and relational databases, including query authoring and optimization, as well as familiarity with various databases such as Hadoop/Hive.
  6. Experience with Spark and data frames in PySpark or Scala.
  7. Strong analytical skills; ability to distill complex data into actionable business insights.
  8. Proven capability to visualize data effectively for influence.
  9. Comfortable working with cloud-based platforms (AWS, Azure, Google).
  10. Familiarity with Google Analytics, Adobe Analytics, and Optimizely is a plus.