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Job Description Summary As a Data Scientist within Manhattan Associate’s Global R&D organization, you will be responsible for researching, evolving, innovating, and productizing advanced analytic methods and Machine Learning capabilities across our industry leading Supply Chain solutions. You will help lead the analysis and solution designs of complex analytical supply chain management problems using a variety of methods from Predictive Analytics, Forecasting, Statistical Analysis and Modeling, Data Science, Simulation, and Machine Learning to ensure that the analytical business tasks and processes used within our solutions have been reliably and accurately modeled and productionalized. You will have access to customers (and their data) to fully gain an understanding about their analytical business needs and opportunities while collaborating with product management, software engineers, and business analysts – as well as with other scientists within our global science group. You will be viewed as an expert and thought leader within the disciplines of Predictive Analytics, Data Science, and Machine Learning. You will have the opportunity to provide leadership and have direct influence on building world-class, industry leading, science-based enterprise software products for global supply chain management. Job Description Responsibilities:
- Provide strong analytical, advanced Data Science and Machine Learning expertise as a key member of our specialized science-based team focused on the research, development, and deployment of Manhattan Associates’ next generation cloud-native Machine Learning platform
- Work with our customers, product management, and application development teams to identify, prototype, develop, and deploy ML-empowered solutions
- Assist product and cloud teams with deployment and lifecycle oversight, performance analysis, issue troubleshooting, tuning and refreshing of production models/pipelines within our ML platform
- Work with large, complex data sets to extract, analyze, visualize, and infer meaningful insights
- Stay current with state-of-the-art techniques and technologies in the field
- Research, experiment, and benchmark new ML use cases, methods, and technologies
- Communicate and document effectively, especially when communicating with non-technical business partners and customers Requirements:
- A graduate degree in a field such mathematics, statistics, computer science, engineering, data science or machine learning, or a related technical field
- Experience with the research and development of ML-based solutions, including the productization, deployment, and lifecycle management of production-level ML models/pipelines
- Proven experience developing ML solutions with any of the Python ML stack: numpy, pandas, scikit-learn
- Knowledge and application of Data Science and ML techniques and best practices, such as but not limited to: o ETL; EDA; Data cleansing/preprocessing, wrangling & transforms, and visualization, … o Feature Extraction, Engineering, & Selection; Dimension reduction techniques, … o Hyperparameter tuning/optimization, ML model performance analysis and tuning, ML model selection techniques, Concept Drift & Model decay, Class imbalance, …
- Application of statistical data analysis and modelling techniques such as linear models, multivariate analysis, stochastic models, sampling methods – including hypothesis testing, model validation -Demonstrated skills in selecting the right statistical tools given a data analysis problem
- Ability to craft new concepts and stay current with academic research
- Solid experience working with databases: SQL and/or NoSQL
- Experience with Linux or Unix operating systems, installing, deploying, maintaining packages
- Strong verbal and written communication skills – including successful experience working with non-technical stakeholders