Role Summary:
Nuuly is seeking a Data Scientist who will have the opportunity to help shape and execute. Nuuly is driven by a state-of-the-art Kafka-based streaming data platform, which provides unfettered access to ata both in real-time and offline. This enables the Data Science team to flexibly iterate on prototypes, and seamlessly integrate new capabilities. You will design, build, evaluate, and support deployment of machine learning models and systems. This includes working closely with other data scientists, ML engineers, and data engineers to perform:

Role Responsibilities:

• ML Modeling: Evaluate and build statistical and machine learning models with an emphasis on business impact and time-to-production. Approaches span from basic regressions to advanced neural networks.
• Analysis: Support opportunities to add value to the business through application of statistical and ML techniques. Quantify the impact of those opportunities and communicate the outcome to peers and leaders.
• ML Deployment: Support Nuuly ML engineers in deploying data science algorithms in production. This includes containerizing algorithm training and inference workflows, building data pipelines, and monitoring already-deployed ML systems.

Role Qualifications:

• Experience with python
• Experience with SQL
• ML tools (TensorFlow, pyTorch, scikit learn, Jupyter Notebooks, etc.)
• Understanding of fundamental Machine Learning concepts, including: experience with train/validation/test sets, overfitting, scoring metrics, neural network applications, etc.
• Experience using ML approaches on real-world applications
• Containerization (Docker)
• Cloud platforms (Google Cloud Platform, etc.)
• Cloud infrastructure (Virtual Machines, Cloud Storage, etc.)
• Performance evaluation of deployed, customer-facing applications and ML models
• Data or ML orchestration frameworks (MLflow, Airflow, etc.)
• A degree in Computer Science, Analytics, Data Science, Mathematics or related field or equivalent job experience

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The Perks:
URBN offers comprehensive Perks & Benefits to employees. Availability and eligibility to specific benefits may be subject to your location and employment status. Benefits include medical, dental, vision, PTO, generous employee discounts, retirement savings and much more! For additional information visit www.urbn.com/work-with-us/benefits
EEO Statement:
Urban Outfitters, Inc. celebrates diversity and is committed to creating an inclusive environment for all employees. We are proud to provide equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, sex (including gender, pregnancy, sexual orientation, and gender identity or expression), religion, creed, age, physical or mental disability, national origin or ancestry, ethnicity, citizenship, service in the uniformed services, genetic information, or any other protected characteristic as established by law. We believe strongly in fostering a safe, fair and respectful work environment. To ensure compliance with our non-discrimination and anti-harassment policies, we offer anti-harassment training to managers and employees

Role Responsibilities:

• ML Modeling: Evaluate and build statistical and machine learning models with an emphasis on business impact and time-to-production. Approaches span from basic regressions to advanced neural networks.
• Analysis: Support opportunities to add value to the business through application of statistical and ML techniques. Quantify the impact of those opportunities and communicate the outcome to peers and leaders.
• ML Deployment: Support Nuuly ML engineers in deploying data science algorithms in production. This includes containerizing algorithm training and inference workflows, building data pipelines, and monitoring already-deployed ML systems

Role Qualifications:

• Experience with python
• Experience with SQL
• ML tools (TensorFlow, pyTorch, scikit learn, Jupyter Notebooks, etc.)
• Understanding of fundamental Machine Learning concepts, including: experience with train/validation/test sets, overfitting, scoring metrics, neural network applications, etc.
• Experience using ML approaches on real-world applications
• Containerization (Docker)
• Cloud platforms (Google Cloud Platform, etc.)
• Cloud infrastructure (Virtual Machines, Cloud Storage, etc.)
• Performance evaluation of deployed, customer-facing applications and ML models
• Data or ML orchestration frameworks (MLflow, Airflow, etc.)
• A degree in Computer Science, Analytics, Data Science, Mathematics or related field or equivalent job experienc