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Director, Data Science

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Columbia, United States Blend360 Full time
Job DescriptionJob DescriptionCompany Description

Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com.

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 build domain-specific knowledge regarding supply chain working closely with stakeholders to understand key business problems and bring Data Science solutions to resolve. You will also advance the development of internal capability for Data Science within supply chain, including Artificial Intelligence (AI), and Machine Learning (ML). Your role will be pivotal in driving awareness of the value Data Science offers our clients with regard to global supply chain.

Summary with focus on communication: Data Scientists at Blend360 work with business leaders to solve our clients’ business challenges. Here at Blend360 we work with clients in marketing, revenue management, customer service, inventory management and many other aspects of modern business. Our Lead Data Scientists have the business acumen to apply Data Scientists to many different business models and situations.

We expect the Data Science Managers to be excellent communicators with the ability to describe complex concepts clearly and concisely. They should be able to work independently in gathering requirements, developing roadmaps, and delivering results.

Teamwork and Leadership: We work as a team and Data Science Managers lead both by mentoring or managing Data Scientists as well as leading by example.

Technical know-how: Our Data Scientists have a broad knowledge of a variety of data and mathematical solutions. Our work includes statistical analyses, predictive modeling, machine learning, and experimental design. We evaluate different sources of data, discover patterns hidden within raw data, create insightful variables, and develop competing models with different machine learning algorithms. We validate and cross-validate our recommendations to make sure our recommendations will perform well over time.

Conclusion: If you love to solve difficult problems and deliver results; if you like to learn new things and apply innovative, state-of-the-art methodology, join us at Blend360.

Responsibilities

  • Advance the development of internal capability for Data Science within supply chain.
  • Build domain specific knowledge regarding supply chain.
  • Ability to provide ethical and positive leadership that motivates direct reports and develops their talent and skillset while achieving results.
  • Directly manage analyst project work and overall performance, including effective career planning; have difficult conversations and deliver constructive feedback with support from senior management.
  • Interview, hire and train new employees.
  • Analyze team KPIs, develop solutions and alternative methods to achieve goals.
  • Build positive and productive relationships with clients for business growth.
  • Understand client needs and customize existing business processes to meet client needs.
  • Promptly address client concerns and professionally manage requests.
  • Work as a strategic partner with leadership teams to support client needs.
  • Work with practice leaders and clients to understand business problems, industry context, data sources, potential risks, and constraints
  • Problem-solve with practice leaders to translate the business problem into a workable Data Science solution; propose different approaches and their pros and cons
  • Work with practice leaders to get stakeholder feedback, get alignment on approaches, deliverables, and roadmaps
  • Develop a project plan including milestones, dates, owners, and risks and contingency plans
  • Create and maintain efficient data pipelines, often within clients’ architecture. Typically, data are from a wide variety of sources, internal and external, and manipulated using SQL, spark, and Cloud big data technologies
  • Assemble large, complex data sets from client and external sources that meet functional business requirements.
  • Build analytics tools to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
  • Perform data cleaning/hygiene, data QC, and integrate data from both client internal and external data sources on Advanced Data Science Platform. Be able to summarize and describe data and data issues
  • Conduct statistical data analysis, including exploratory data analysis, data mining, and document key insights and findings toward decision making
  • Train, validate, and cross-validate predictive models and machine learning algorithms using state of the art Data Science techniques and tools
  • Document predictive models/machine learning results that can be incorporated into client-deliverable documentation
  • Assist client to deploy models and algorithms within their own architecture
Qualifications

  • MS degree in Statistics, Math, Data Analytics, or a related quantitative field
  • 5+ years Professional experience in Advanced Supply Chain Data Science 
  • Experience with one or more Advanced Data Science software languages (R, Python, Scala, SAS) 
  • Proven ability to deploy machine learning models from the research environment (Jupyter Notebooks) to production via procedural or pipeline approaches
  • Experience with SQL and relational databases, query authoring and tuning as well as working familiarity with a variety of databases including Hadoop/Hive
  • Experience with spark and data-frames in PySpark or Scala
  • Strong problem-solving skills; ability to pivot complex data to answer business questions. Proven ability to visualize data for influencing.
  • Comfortable with cloud-based platforms (AWS, Azure, Google)
  • Experience with Google Analytics, Adobe Analytics, Optimizely a plus