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Senior Data Scientist

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About the Job

ketteQ is a supply chain planning and automation platform provided as a Software as a Service solution. As we continue to grow and evolve, we’re looking for a highly organized and detailed oriented Senior Data Scientist to join our team and play a pivotal role in developing the best-in-class supply chain planning and automation platform. This job comes with a very attractive compensation package and work-from-home benefits. You will get to work with large global brands and a highly experienced team. If you are high-energy, motivated, and an initiative-taking individual then this could be a fantastic opportunity for you. Candidates must meet the following qualifications:

Duties & Responsibilities

  • Jointly drive the Advanced Data Science Solutions roadmap; hands-on, leading by example with proof of concepts, reference implementations
  • Lead design and development components of the advanced data analytics solution ecosystem of ketteQ to solve complex business problems
  • Develop scalable advanced data analytics applications
  • Provide strategic focus on the latest, cutting edge data science and AI/ML methodologies and its applications to the supply chain industry
  • Recommend processes, services, software and other tools to support business objectives pertinent to Data Science/AI/ML, as well as relevant infrastructure technologies


  • BS/MS/PhD in Business Analytics, Data Science,Mathematics/Statistics or a related field; Industrial Engineering, Supply Chain, Operations Research educational backgrounds will be applicable as well with appropriate experience
  • At least 7 years of experience applying advanced data sciences,AI/ML techniques to real world problems, supply chain or related applications is a strong plus
  • Ability to understand business and supply chain needs and leverageit to effectively design product solutions to drive business value
  • Strong math skills (e.g. statistics, algebra)
  • Knowledge of advanced statistical techniques and concepts(regression, properties of distributions, statistical tests and proper usage,etc.) and experience with applications.
  • Knowledge of a variety of machine learning techniques (clustering,decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
  • At least 2 years of experience working with time series forecasting algorithms. Completed Projects on Time Series Forecasting, beyond statistical algorithms like ARIMA
  • In-depth understanding of classical statistical forecasting algorithms like ARIMA, ETS, etc. as well as new age algorithms like Prophet.
  • Proven experience in handling Time Series Forecasting using standard Regression algorithms like Linear Regression, Gradient Boosted Decision Trees, Random Forest, etc.
  • Strong conceptual knowledge of AI/ML techniques and eager to learn and adopt new approaches and methodologies.
  • Good practical knowledge with Sequence Models in Deep Learning,like LSTMs, Transformers, etc. especially in the context of Time Series Forecasting.
  • Understanding of the software development life cycle, including capturing product requirements, review of functional specifications,development of solutions, developing test plans, testing, user training, and deployment
  • Expert ability to write robust code in Python. Familiarity with Scala, Java or C++ is an asset. Knowledge of R is a strong asset
  • Proficiency with at least one ML Framework such as TensorFlow,Keras, PyTorch, etc.
  • Hands-on experience with relational databases (e.g. PostgreSQL,MySQL), BI/data analysis tools
  • Experience with distributed data/computing tools: Map/Reduce,Hadoop, Hive, Spark, etc
  • Experience using conventional release and deployment workflows and policies and working in GitHub/Bitbucket
  • Experience working in Linux environments with containerization technologies (Docker, Kubernetes, AWS Fargate, Argo) and major cloud services(AWS, GCP, Azure)
  • Ability to handle complex situations with little to no guidance
  • Explore data and communicate insights clearly to non-technical as well as technical audience
  • Experience in retail, CPG, e-commerce, or supply chain would be considered an asset
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