Data Scientist - Demand Planning D430

• 4+ years of experience in the field of Data Science and Analytics preferably in CPG or Manufacturing Domain
• Bachelors Degree in Computer Science, Mathematics, Statistics, Economics, Engineering or related field
• Strong programming skills and experience in using Python for Data Science
• Strong analytical techniques, data mining knowledge and proficiency in handling and processing large amounts of data is needed
• Deep Knowledge of statistical and machine learning algorithms for Time Series Forecasting
• Experience in time series forecasting at scale (Like XG Boost, CatBoost, Light GBM , Neural Networks etc)
• Experience in custom EDA (Exploratory Data Analysis) and Output Validations
• Experience in building scalable ML frameworks for demand sensing including Identifying and collecting relevant input data, feature engineering, tuning, and testing.
• Experience in applied analytical methods in the field of Supply chain and planning like demand planning, supply planning, market intelligence, optimal assortments/pricing/inventory etc.
• Strong presentation and communications skills

Nice to have:

• o9 Platform Experience
• Experience with SQL, databases and ETL tools or similar is optional but preferred
• Exposure to distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark or related Bid Data technologies
• Experience in implementing supply chain planning applications will be a plus

What you will do:

• Design and operationalize various kinds of descriptive, predictive and prescriptive analytics and data science relevant in the supply chain planning space.
• Understand business; identify areas of improvement opportunities; collect and analyze data; build models in Python; and present results & insights using the o9 platform.
• Apply a variety of machine learning techniques (clustering, regression, ensemble learning, neural nets, time series etc.) to their real-world advantages/drawbacks
• Develop and deploy models for demand sensing/forecasting, Anomaly detection, Simulation and stochastic models, Market Intelligence etc
• Use latest advancements in AI/ML to solve business problems
• Analyze problems by synthesizing complex information, evaluating alternate methods, and articulating the result with the relevant assumptions/reasons
• Application of common business metrics and the ability to generate new ones as needed
• Work collaboratively with Clients, Project Management, Solution Architects, Consultants and Data Engineers to ensure successful delivery of o9 projects