- The data scientists will be responsible for modeling complex problems, discovering insights and identifying opportunities through the use of statistical, machine learning, algorithmic, data mining and visualization techniques.

- They are also responsible for moving the models to production environment and automate with appropriate drift monitoring and model improvement process.

- They will need to collaborate effectively with internal stakeholders and cross-functional teams to understand requirements, solve problems, create operational efficiencies, and deliver successfully against high organizational standards.

- The candidates should be able to apply a breadth of tools, data sources and analytical techniques to answer a wide range of high-impact business questions and present the insights in concise and effective manner.

- The candidates should be an effective communicator capable of independently driving issues to resolution, communicating insights to non-technical audiences and documenting the artifacts. This is a high impact role with goals that directly impacts the bottom line of the business.

Key Deliverables/Outcomes :

- Development of accurate and reliable workforce forecasting models.

- Automate training and forecasting by deploying data and model pipelines on AWS cloud infrastructure with drift monitoring with emphasis on accuracy and speed

- Building Project launch impact estimates model using tools such as of analytics, time series, probability and deep learning.

- Reduce MAPE (forecasting accuracy) across different risk functions.

- Automate data ingestion for Project inputs from Excel to forecasting models

- Improved data quality through rigorous cleaning and transformation processes and automating them.

- Clear documentation of the code and artifacts.

- Actionable insights derived from data analysis to support strategic decisions.

- Experiment with latest forecasting algorithms & processes to optimize existing modeling infrastructure.

Qualifications Needed :

- 5+ years of data scientist experience, preferably with Forecasting systems & Operational Research

- 3+ years of data querying languages (e.g. SQL) and scripting languages (e.g. Python) experience

- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience

- 3+ years of AWS cloud experience building end-to-end products deploy, monitor and update them using tools such as Amazon SageMaker pipelines and docker containers.

- Experience applying theoretical models in an applied environment

- Understanding of demand forecasting & impact on operational capacity planning

- Knowledge of time series models and deep learning for time series are an asset.