VP/Director Data Science

1 month ago

Calgary, Canada Blend360 Full time
Job Description

At Blend360 we want to ensure that our clients have access to the data, insights, and innovations required to deliver against their Supply Chain Strategy. We are seeking a VP of Data Scientist, who can help advance the field within the Supply Chain Organization and deliver meaningful solutions that strive to solve our clients’ biggest challenges.

Accountabilities

As a Supply Chain Data Scientist, you will:

  1. Build domain-specific knowledge regarding supply chain, working closely with stakeholders to understand key business problems.
  2. Bring Data Science solutions to resolve those problems.
  3. Advance the development of capability for Data Science within supply chain, including Artificial Intelligence (AI) and Machine Learning (ML).
  4. Drive awareness of the value Data Science offers our clients with regard to global supply chain.
Responsibilities
  1. Advance the development of capability for Data Science within supply chain.
  2. Build domain-specific knowledge regarding supply chain.
  3. Provide ethical and positive leadership that motivates direct reports and develops their talent and skillset while achieving results.
  4. Directly manage analyst project work and overall performance, including effective career planning; have difficult conversations and deliver constructive feedback with support from senior management.
  5. Interview, hire, and train new employees.
  6. Analyze team KPIs, develop solutions, and alternative methods to achieve goals.
  7. Build positive and productive relationships with clients for business growth.
  8. Understand client needs and customize existing business processes to meet client needs.
  9. Promptly address client concerns and professionally manage requests.
  10. Work as a strategic partner with leadership teams to support client needs.
  11. Work with practice leaders and clients to understand business problems, industry context, data sources, potential risks, and constraints.
  12. Problem-solve with practice leaders to translate the business problem into a workable Data Science solution; propose different approaches and their pros and cons.
  13. Work with practice leaders to get stakeholder feedback, get alignment on approaches, deliverables, and roadmaps.
  14. Develop a project plan including milestones, dates, owners, risks, and contingency plans.
  15. Create and maintain efficient data pipelines, often within clients’ architecture.
  16. Assemble large, complex data sets from client and external sources that meet functional business requirements.
  17. Build analytics tools to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
  18. Perform data cleaning/hygiene, data QC, and integrate data from both client internal and external data sources on Advanced Data Science Platform.
  19. Summarize and describe data and data issues.
  20. Conduct statistical data analysis, including exploratory data analysis, data mining, and document key insights and findings toward decision making.
  21. Train, validate, and cross-validate predictive models and machine learning algorithms using state-of-the-art Data Science techniques and tools.
  22. Document predictive models/machine learning results that can be incorporated into client-deliverable documentation.
  23. Assist clients to deploy models and algorithms within their own architecture.
Qualifications
  1. MS degree in Statistics, Math, Data Analytics, or a related quantitative field.
  2. At least 5+ years of professional experience in Advanced Supply Chain Data Science.
  3. Experience with one or more Advanced Data Science software languages (R, Python, Scala, SAS).
  4. Proven ability to deploy machine learning models from the research environment (Jupyter Notebooks) to production via procedural or pipeline approaches.
  5. Experience with SQL and relational databases, query authoring, and tuning as well as working familiarity with a variety of databases including Hadoop/Hive.
  6. Experience with spark and data-frames in PySpark or Scala.
  7. Strong problem-solving skills; ability to pivot complex data to answer business questions.
  8. Proven ability to visualize data for influencing.
  9. Comfortable with cloud-based platforms (AWS, Azure, Google).
  10. Experience with Google Analytics, Adobe Analytics, Optimizely is a plus.
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