Senior Applied Scientist (AI/ML - Pricing)

Nir Yu; México; A distancia; MXN 700,000 - 900,000; Jornada completa

Descripción de la vacante

A leading retail analytics company located in Mexico is seeking a Senior Applied Scientist (AI/ML - Pricing) to develop and deploy innovative machine learning solutions for retail pricing. The ideal candidate will have a robust background in machine learning and optimization, with at least 5 years of industry experience. This role involves mentoring junior members, collaborating across teams, and influencing technical direction to solve complex pricing challenges. A PhD or Master's degree is required.

Formación

• 5+ years industry experience in applied Machine Learning.
• Experience building and managing ML models in production.
• Deep understanding of ML best practices and algorithms.

Responsabilidades

• Research and develop ML models for pricing challenges.
• Analyze large datasets for pricing strategies.
• Collaborate with stakeholders on data-driven solutions.

Conocimientos

Machine learning

Optimization

Statistical analysis

Data engineering

Big data tools

Python

SQL

Educación

PhD or Master's degree in Computer Science, ML, Statistics, Operation Research or related field

Herramientas

TensorFlow

Keras

PyTorch

Scikit-Learn

Apache Beam

Apache Kafka

Spark

Descripción del empleo

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.