Data Scientist – Production Machine Learning and Platform Optimization

We are looking for a Data Scientist to play a key role in enhancing and scaling an existing analytics platform that supports recommendation systems and product availability use cases. This position focuses on improving the performance, reliability, and scalability of machine learning solutions already in production, while also contributing to the underlying data infrastructure that supports them.

In this role, you will work closely with cross-functional teams including product, engineering, and business stakeholders to deliver practical, high-impact improvements to existing models and data workflows.

Key Responsibilities

Machine Learning and Platform Enhancement

• Evaluate current machine learning models, data pipelines, and system architecture to identify opportunities for improvement

• Enhance model performance and reliability through iterative development, testing, and deployment

• Design and implement production-ready solutions with a focus on scalability, maintainability, and performance

• Establish clear evaluation frameworks to measure model effectiveness and business impact

• Communicate insights, recommendations, and tradeoffs to both technical and non-technical audiences

• Monitor model performance over time, including data quality, drift, and system health, and drive ongoing improvements

Data and Platform Engineering

• Support the development and optimization of data pipelines used for model training and inference

• Build and maintain structured datasets and features used in machine learning workflows

• Contribute to modern data architecture patterns, including batch and near real-time processing

• Assist in integrating models into applications through APIs and automated workflows

Qualifications Required

• Bachelor’s degree in a technical field such as Computer Science, Statistics, Mathematics, or equivalent experience

• 4 or more years of experience in data science or applied machine learning roles with production exposure

• Strong experience developing and evaluating machine learning models using tools such as scikit-learn, gradient boosting frameworks, or time series techniques

• Proficiency in Python and SQL

• Experience deploying models into production environments and iterating based on performance and feedback

• Ability to work within existing systems and incrementally improve functionality without disrupting operations

Preferred

• Experience collaborating with data engineering or MLOps teams on production systems

• Familiarity with building and maintaining data pipelines and working with large-scale datasets

• Experience with distributed data processing tools such as Spark or similar technologies

• Exposure to modern cloud-based data platforms and workflows

• Familiarity with model lifecycle management, monitoring, and reproducibility practices

• Exposure to emerging AI tools and technologies

Professional Attributes

• Strong problem-solving and analytical thinking skills

• Ability to clearly communicate technical concepts to a variety of stakeholders

• Comfortable working in a fast-paced and evolving environment

• Collaborative mindset with the ability to work across teams

• Self-driven and proactive, with a strong sense of ownership