Data Scientist/ Machine Learning Engineer - Job Overview Identify Priorities: Collaborate closely with product, engineering, data analytics, and business stakeholders to identify and prioritize the most impactful machine learning opportunities that align with our strategic goals. Develop Models: Lead the end-to-end development of machine learning models from data collection and feature engineering to algorithm selection, training, tuning, and validation. Deploy Systems: Develop production-grade code and systems to deploy, serve, and monitor machine learning models at scale, ensuring reliability and performance. Evaluate Performance: Define key performance metrics, establish robust monitoring frameworks, analyze model performance in production, and drive continuous improvement through iteration and experimentation. This is a critical role where you will have the unique opportunity to shape the future of data science and machine learning. Key Responsibilities: Data Collection - Collect relevant data for machine learning models, considering data quality and availability. Model Development - Design and implement machine learning algorithms, selecting the best approach based on problem complexity and available resources. System Integration - Develop and integrate machine learning models into existing systems, ensuring seamless interactions and optimal performance. Evaluation and Improvement - Continuously evaluate model performance, identifying areas for improvement and implementing changes to enhance overall effectiveness. This role requires strong collaboration with cross-functional teams, excellent problem-solving skills, and a passion for driving innovation through data-driven insights. What We Offer: A dynamic work environment with opportunities for growth and professional development. Competitive compensation and benefits packages. Flexible work arrangements to support work-life balance. Opportunities to collaborate with talented professionals and contribute to cutting-edge projects.