Sr.Data and Applied Scientist

As a Senior Data and Applied Scientist, you will work with Pattern's Data Science team to curate and analyze data and apply machine learning models and statistical techniques to solve business problems across the organization. Most of your work will revolve around forecasting.

What youll do:

• Design, build, and maintain predictive machine learning and statistical models to optimize solutions across Pattern.
• Research, experiment, and present novel forecasting approaches.
• Communicate potential model improvements, data needs, and opportunities across relevant business units and developer teams.
• Continuously optimize the quality of our machine learning models, especially for key forecasting metrics like bias and WAPE.
• Conduct research to integrate new data sources, innovate in feature engineering, fine-tune algorithms, and enhance data pipelines for robust model performance.
• Analyze large datasets to extract actionable insights that guide business decisions.
• Work closely with data science teams across different regions (US and India), ensuring seamless collaboration and knowledge sharing.
• Follow code standards and best practices.
• Perform root cause analysis to determine accuracy defects to drive improvements.

What were looking for:

• Bachelor's or Master's in Data Science, Computer Science, Statistics, Economics, or a related field.
• 2-6 years of industry experience in building and deploying machine learning solutions.
• 1-3 years of industry experience in demand forecasting or complex forecasting scenarios.
• Excellent communication (written and verbal) across departments with different levels of machine learning knowledge
• Strong data manipulation and programming skills in Python and SQL and hands-on experience with libraries such as Pandas, Numpy, Scikit-Learn, XGBoost.
• Strong problem-solving skills and an ability to analyze complex data.
• In depth expertise in a range of machine learning and statistical techniques such as linear and tree-based models along with understanding of model evaluation metrics.
• Experience with Git, AWS, and deep learning models is advantageous.