Blanc Labs

Data Scientist

• Partner with the AI Technical Product Manager to support data-driven decision-making across AI initiatives
• Analyze datasets to assess the potential functional and business impact of proposed features
• Define success metrics and measure post-implementation performance to evaluate actual impact
• Provide actionable insights that inform product roadmap, prioritization, and ROI
• Collaborate with ML Engineers to identify machine learning opportunities using proprietary datasets
• Design and execute experiments (A/B testing, hypothesis testing) to validate product and AI feature effectiveness
• Build dashboards, reports, and visualizations to communicate insights to technical and non-technical stakeholders
• Ensure data quality, integrity, and proper usage across projects.

Senior Data Scientist

Machine Learning Engineer

• Design, build, and deploy end-to-end machine learning models using proprietary datasets
• Own the full ML lifecycle, including data preparation, feature engineering, model training, evaluation, and validation
• Collaborate with Data Scientists to identify and prioritize high-impact ML opportunities
• Work with Principal AI Engineers to ensure models align with system architecture and engineering best practices
• Package and expose trained models as scalable APIs for consumption by AI Engineers and downstream systems
• Build and maintain model pipelines, including versioning, retraining, and continuous improvement workflows
• Monitor model performance in production, ensuring accuracy, reliability, and relevance over time
• Troubleshoot model drift, data quality issues, and performance bottlenecks

Senior Machine Learning Engineer

AI Engineer

• Build and deliver AI-powered features based on functional and technical requirements provided by TPMs and Principal AI Engineers
• Develop end-to-end AI solutions, including backend services, APIs, and application-layer integrations
• Integrate large language models (LLMs) into production systems, ensuring performance, scalability, and reliability
• Design, test, and optimize prompts to improve output quality and consistency
• Collaborate with ML Engineers to embed models trained or fine-tuned on proprietary data into applications
• Participate in model evaluation and validation, ensuring outputs meet quality, accuracy, and performance benchmarks
• Support testing and deployment of AI features through CI/CD pipelines
• Troubleshoot and refine AI behaviours in real-world production scenarios.

Senior AI Engineer

• Design, build, and deploy end-to-end machine learning models using proprietary datasets
• Own the full ML lifecycle, including data preparation, feature engineering, model training, evaluation, and validation
• Collaborate with Data Scientists to identify and prioritize high-impact ML opportunities
• Work with Principal AI Engineers to ensure models align with system architecture and engineering best practices
• Package and expose trained models as scalable APIs for consumption by AI Engineers and downstream systems
• Build and maintain model pipelines, including versioning, retraining, and continuous improvement workflows
• Monitor model performance in production, ensuring accuracy, reliability, and relevance over time
• Troubleshoot model drift, data quality issues, and performance bottlenecks.