Overview

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Work Shift


1ST SHIFT (United States of America)

Summary


The Director of Data Science is a role in the Insights as a Service organization and reports to Vice President of IT Architecture & Analytics. They will play a pivotal role in staffing, leading, planning, executing and delivering machine learning-based projects. The bulk of the work will be in machine learning (ML) modelling, management and problem analysis, data exploration and preparation, data collection and integration, operationalization. Tyson foods is expanding and building up its data analytics practice, the role must also cover the leadership of related roles of data engineer and DevOps engineer at times. Data Science is a key focus for Tyson Foods, this role is foundational in driving insights, advancing technology and enabling key decisions.

Requirements


Education: A bachelor’s or master’s degree in computer science, data science, operations research, statistics, applied mathematics, or a related quantitative field or equivalent work experience such as, economics, engineering and physics is preferred. Alternate experience and education in equivalent areas such as economics, engineering or physics, is acceptable. Experience in more than one area is strongly preferred. Candidates must have a specialization in ML, AI, cognitive science or data science.

Experience


Candidates should have 12 or more years of relevant project experience in successfully launching, planning, executing and leading data science projects. Preferably in the domains of forecasting, computer vision, risk modelling, customer behavior prediction, quality assessment, and factory automation.

  • A specialization in text analytics, image recognition, graph analysis or other specialized ML techniques such as deep learning, etc., is required.
  • Ideally, the candidates are adept in agile methodologies and well-versed in applying DevOps/MLOps methods to the construction of ML and data science pipelines.
  • Candidates should exhibit significant project experience in applying ML and data science to business functions such as website optimization, financial risk analytics, logistics, manufacturing, customer journey analytics, marketing analytics, quality assessment, production automation, e-commerce platforms, warehouse logistics, physical robot control, process control, target marketing, churn management, etc..
  • Candidates need to demonstrate that they were instrumental in launching significant data science projects.
  • Candidates should have demonstrated the ability to manage data science projects and diverse teams.
  • Experience leading program level initiatives through cross-functional, matrix environments using strong program and project management tools and practices.
  • Experience negotiating with vendors; ability to manage external relationships with contracting firms and application developers.
  • Experience developing business cases with an emphasis on ROI and calculated Returns.

IT Knowledge/Skills

  • [Basic/substantial/expert] Coding knowledge and experience in several languages: for example, [R, Python/Jupyter, SAS, Java, Scala, C++, Excel, MATLAB, etc.].
  • [Optional] Experience with popular database programming languages including SQL, PL/SQL, others for relational databases and upcoming nonrelational databases such as NoSQL/Hadoop-oriented databases such as MongoDB, Cassandra, others.
  • Experience with distributed data/computing tools: MapReduce, Hadoop, Hive, Kafka, also MySQL, and so on
  • Experience of working across multiple deployment environments including cloud, on-premises and hybrid, multiple operating systems and through containerization techniques such as Docker, Kubernetes, AWS Elastic Container Service, and others

Machine Learning and Data Science Knowledge/Skills

  • Experience in one or more of the following commercial/open-source data discovery/analysis platforms: RStudio, Machine Learning, Amazon SageMaker, Google Cloud ML, SAP Predictive Analytics.
  • Expertise in solving vision, text analytics, failure prediction, propensity to buy problems is required.
  • Knowledge and experience in statistical and data mining techniques: generalized linear model (GLM)/regression, random forest, boosting, trees, text mining, hierarchical clustering, deep learning, convolutional neural network (CNN), recurrent neural network (RNN), T-distributed Stochastic Neighbor Embedding (t-SNE), graph analysis, etc..

Interpersonal Skills and Characteristics

  • All candidates must be self-driven, curious and creative.
  • Candidates must demonstrate the ability to work in diverse, cross-functional teams in a dynamic business environment.
  • Candidates should be confident, energetic self-starters, with strong moderation and communication skills.
  • Candidates should exhibit superior presentation skills, including storytelling and other techniques to guide and inspire.
  • Candidates should have a track record in launching innovative projects, gaining the respect of stakeholders at all levels and roles within the company.

Supervisory


Lead team of 7-10 senior skilled professionals to accomplish ongoing objectives.

Ability to manage change and diversity.

Emotional intelligence skills and awareness.

Ability providing constructive feedback to team members.

Ability work in an onshore / offshore environment and at times with a managed IT service provider

Travel: Some international travel may be required. Amount of travel will be determined by individual project requirements.

When completing a Tyson Foods employment application, be sure to complete all tasks listed on the candidate home page. If not, you will see a message that there are 1 or more task(s) that require attention. Applicants for hourly production positions must complete the task to provide additional information to be considered from employment.

Tyson is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will be considered without regard to race, national origin, color, religion, age, genetics, sex, sexual orientation, gender identity, disability or veteran status.