The Role:

The Senior Applied Scientist (AI/ML - Pricing) plays a crucial role in the development  and deployment of innovative machine learning solutions for retail pricing that includes  understanding various customer and operational business constraints and translating  complex real-world problems into well-defined mathematical objectives. The ability to  research, develop, and implement machine learning models for pricing and price  optimization strategy is crucial for the success of this role.  

You should have a robust background in machine learning, optimization, forecasting, and causal inference, particularly within pricing applications or closely related fields. You will be involved in every stage of the ML development pipeline - from data acquisition and ingestion to analysis, prototyping and deployment. You should be able to thrive and succeed in an entrepreneurial setting, working collaboratively in a fast-paced environment with multiple stakeholders. 

Roles and Responsibilities:

• Research and develop machine learning and statistical models and apply optimization to solve complex pricing challenges.
• Analyze large and complex datasets to derive insights that inform key algorithmic strategies for pricing.
• Employ state-of-the-art Machine Learning methodologies and frameworks to develop robust and scalable models.
• Develop and maintain clean, efficient, and scalable code that meets industry standards.
• Communicate ideas and results effectively, verbally and in writing, to technical and non-technical audiences.
• Collaborate with key stakeholders in the development of data-driven solutions and deployable products. 
• Contribute to the company's intellectual property and technical leadership through patents and publications at top-tier conferences and journals.
• Influence technical direction and take ownership of key components of systems and solutions, ensuring that they meet the needs of the business.
• Mentor junior team members to help establish team domain expertise.

Minimum Requirements:

• PhD or Masters degree in Computer Science, Machine Learning, Statistics, Operation Research or related field
• 5+ years of industry experience in applied Machine Learning, 3+ years experience in building, deploying, and managing machine learning and deep learning models in production environments at scale
• Deep understanding of ML best practices (A/B testing, training/serving pipelines, feature engineering etc), algorithms/techniques (gradient boosting, deep neural networks, optimization, regularization), and experiment design
• Proficiency with scientific libraries in Python (numpy, pandas, polars) and Machine Learning tools and frameworks (Scikit-Learn, Tensorflow, Keras, PyTorch) 
• Strong data engineering skills and experience working with large scale datasets
• Experience with big data tools (Apache Beam, Apache Kafka, Spark)
• Experience with cloud technologies AWS, GCP or Azure
• Fluency in Python, SQL

Preferred Requirements: 

• PhD preferred (CS, ML, AI, Statistics, Operation Research or related field)
• Background in applying ML techniques to solve real-world business problems in the retail sector, especially pricing.
• Familiarity with MLOps tools and pipelines.
• Impact-focused and passionate about delivering high-quality models.
• Demonstrated leadership experience, with the ability to lead and inspire a team.