Jerry.ai is looking for an Applied Data Scientist to ensure the cleanliness, accuracy, and reliability of their data, which is foundational for all data analysis and critical business decisions. The role aims to transform messy datasets into usable assets for building models and delivering tangible value in real-world applications.
Requirements
- Strong proficiency in Python (Pandas/NumPy) and SQL for complex querying and data manipulation
- Hands-on experience with data cleaning techniques and data validation frameworks
- Familiarity with data visualization tools to help identify and communicate data issues
Responsibilities
- Participate in the full modeling lifecycle, from statistical analysis and experimentation to building, validating, and iterating on machine learning models that address critical business challenges
- Own the data foundation by preparing, cleaning and transforming raw, complex data into high-quality features for modeling.
- Proactively identify and handle missing values, outliers, and inconsistencies
- Investigate data discrepancies (tracking bugs, ETL errors, definitional issues) and design automated frameworks to ensure data accuracy
- Act as a strategic liaison, collaborating with data Engineering and product teams to drive the data strategy and definition of our centralized feature store, ensuring it becomes the 'single source of truth' for all ML models
- Create and maintain clear, authoritative documentation for data sources, cleaning processes, and variable definitions
Other
- Meticulously detail-obsessed
- Passionate about the foundational layer of all data analysis: ensuring our data is clean, accurate, and reliable.
- A detective at heart, driven by a deep curiosity to understand complex data systems
- Finds immense satisfaction in transforming messy, ambiguous datasets into pristine assets that drive critical business decisions.
- Obsessed with Details: You have an exceptional eye for detail and a low tolerance for errors. You believe that accuracy and precision are non-negotiable
