Job Description

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As a 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 optimize advertising spend on ecommerce platforms.
What you'll do:• Design, build, and maintain machine learning and statistical models to optimize advertising campaigns to improve search visibility and conversion rates on ecommerce platforms.
• Continuously optimize the quality of our machine learning models, especially for key metrics like search ranking, keyword bidding, CTR and conversion rate estimation
• Conduct research to integrate new data sources, innovate in feature engineering, fine-tuning algorithms, and enhance data pipelines for robust model performance.
• Analyze large datasets to extract actionable insights that guide advertising decisions.
• Work closely with teams across different regions (US and India), ensuring seamless collaboration and knowledge sharing.
• Dedicate 20% of time to MLOps for efficient, reliable model deployment and operations.


What we're looking for:• Bachelor's or Master's in Data Science, Computer Science, Statistics, or a related field.
• 1-3 years of industry experience in building and deploying machine learning solutions.
• 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, Docker, and MLFlow is advantageous.


Additional Pluses:
Portfolio: An active Kaggle or Github profile showcasing relevant projects.
Domain Knowledge: Familiarity with advertising and ecommerce concepts, which would help in tailoring models to business needs.

Pattern is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.