<p><b>What role you will play in team:</b> You will be a key member of our data science team, contributing to the development and implementation of machine learning models for fraud detection.  You'll collaborate with engineers and product managers to ensure seamless integration.</p><p><b>What you will do:</b> You will be responsible for building, testing, and deploying machine learning models. This will involve data cleaning, feature engineering, model selection, and performance evaluation.</p><p><b>Key responsibility:</b><ul><li>Develop and implement machine learning algorithms for fraud detection.</li><li>Clean and preprocess large datasets.</li><li>Engineer features to improve model performance.</li><li>Select and train appropriate machine learning models.</li><li>Evaluate model performance and identify areas for improvement.</li><li>Deploy models into production environments.</li><li>Collaborate with engineers and product managers.</li></ul></p><p><b>Required Qualification and Skills:</b><ul><li>Master's degree in Data Science, Statistics, or a related field.</li><li>Proficiency in Python or R programming languages.</li><li>Experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch.</li><li>Strong understanding of statistical modeling and machine learning techniques.</li><li>Experience with SQL and big data technologies (e.g., Hadoop, Spark) is a plus.</li></ul></p><p><b>Benefits Included:</b><ul><li>Competitive salary and benefits package.</li><li>Opportunities for professional development.</li><li>Collaborative and supportive work environment.</li><li>Health insurance, Paid Time Off</li></ul></p><p><b>A Day in the Life:</b>  You'll start your day by reviewing model performance metrics, then work on feature engineering and model improvement.  Collaboration with the team follows, followed by code reviews and deployment preparations.</p>