Senior Data Scientist, TBAuctions

• The ideal candidate will have a strong background in data science, machine learning, and software engineering to effectively translate models into scalable solutions
• 7+ years of industry experience
• Master's in Computer Science, Statistics, Mathematics, or related field
• Proven experience in developing and deploying machine learning models in production environments
• Proven organizational and project management skills
• Expert in programming languages such as Python and Pyspark
• Strong understanding of data manipulation, statistical analysis, and machine learning algorithms (MLlib, Scikit-learn, TensorFlow/Pytorch, GenAI)
• Enthusiasm for AI use cases and innovation
• Excellent communication skills and ability to work in a collaborative team environment

Desirable

• We are seeking a Data Scientist to join our team with a primary focus on building machine learning models and deploying them into production environments
• Machine Learning Model Development:
• Develop and implement machine learning models to address business challenges
• Conduct data analysis, feature engineering, and model selection to optimize performance
• Model Productionization:
• Deploy machine learning models into production systems for real-time applications
• Collaborate with software engineers to integrate models into existing infrastructure
• Performance Monitoring, Evaluation and Optimization:
• Monitor model performance in production, investigate drift, and iterate to improve evaluation metrics
• Conduct A/B testing and validation to assess model impact and reliability
• Cross-functional Collaboration:
• Work closely with cross-functional teams to understand business requirements and translate them into technical solutions
• Communicate complex technical concepts to non-technical stakeholders effectively
• Documentation and Reporting:
• Create reproducible, versioned documentation of modeling development, experiments, and results
• Produce reports and presentations that communicate methods, findings, and recommendations