M/L, Python and experience with machine and deep learning methods (e.g. XGBoost, LSTM, Transformers) Familiarity with PySpark, Git, Databricks, Azure. Familiarity with optimal designs and optimization algorithms Familiarity with visualization and rapid prototyping tools (e.g. R Shiny, Power BI)
Responsibilities:
- Work hands-on along with team members to deliver the portfolio of projects aligned to supply chain analytics needs
- Identify, acquire, and engineer feature data sets with potential to address customer needs
- Leverage data science and data engineering resources, capabilities, and best practices
- Apply machine learning/statistical models and other computational approaches to extract insights from large datasets in manufacturing systems.
- Partner with senior engineers/leaders to understand needs and formulate high-impact projects that you and your team members will deliver
- Collaborate with other team members to ensure scientific rigor in projects under your purview
- Communicate insights derived from complex data analysis into simple conclusions that empower leadership to drive action
Educational Qualifications
- M.S or Ph.D. degree in Computer Science, Statistics, Applied Statistics, Operations Research or related highly quantitative fields.
Requirements:
What expertise have you grown? What do you bring to the table?
- Proven ability to independently translate scientific needs into data science problems by asking right questions, and delivering solutions by identifying necessary data, building models, and producing actionable results
- Solid understanding of emerging analytics/machine learning approaches and how they can be deployed to drive value in an industry setting
- In-depth expertise in applying machine and deep learning methods (e.g. XGBoost, LSTM, Transformers)
- Experience initiating, developing, and leading data science projects/efforts as a solo contributor
- Programming experience with at least one scientific programming language (e.g. Python, R), with the desire and ability to learn additional languages as needed
- Strong familiarity with libraries such as Pandas, Scikit-learn, Keras, TensorFlow and PyTorch.
- Critical thinking, strong problem-solving skills, flexibility, and willingness to learn
Preferred Qualifications:
- Publications in reputed international journals and/or conferences on application on machine/deep learning methods
- Familiarity with PySpark, Git, Databricks, Azure.
- Familiarity with optimal designs and optimization algorithms
- Familiarity with visualization and rapid prototyping tools (e.g. R Shiny, Power BI)