What are the responsibilities and job description for the Senior Data Scientist position at ketteQ?
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 detail-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 their 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.
Requirements
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 leverage it to effectively design product solutions to drive business value.
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.
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 audiences.
Experience in retail, CPG, e-commerce, or supply chain would be considered an asset.
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