Mr. Cooper Group – Machine Learning Engineer

Job Description

Mr. Cooper Group is seeking a talented and innovative Machine Learning Engineer to join its growing technology team in India. In this role, you will be responsible for building advanced machine learning solutions that support the organization’s mission of making homeownership possible for millions of customers. The position involves working with large datasets, developing predictive models, and creating intelligent systems that improve decision-making across business operations. As a Machine Learning Engineer, you will play a key role in analyzing complex data, designing scalable algorithms, and transforming raw data into actionable insights that enhance customer experiences and operational efficiency.

The role requires a strong understanding of data science, artificial intelligence, and machine learning pipelines. You will work closely with cross-functional teams including data engineers, software developers, and business analysts to implement machine learning models into production environments. The position focuses on building robust systems capable of handling real-world financial and operational challenges. From performing exploratory data analysis to deploying optimized models, you will contribute to the development of reliable AI-driven applications that power smarter mortgage services and digital financial solutions.

Additionally, the Machine Learning Engineer will be responsible for improving model performance, ensuring scalability, and implementing monitoring strategies to maintain model accuracy over time. This role offers the opportunity to experiment with advanced machine learning techniques, research innovative AI approaches, and mentor junior engineers within the team. By leveraging modern tools and best practices, you will help Mr. Cooper Group stay at the forefront of data-driven innovation while delivering meaningful technological advancements in the financial services industry.

Roles & Responsibilities

• Conduct in-depth exploratory data analysis (EDA) to identify trends, patterns, and correlations within large datasets. Use visualization techniques and statistical tools to derive meaningful insights that support machine learning model development.

• Design, develop, and implement machine learning models tailored to solve complex business problems. Evaluate different algorithms and select the most appropriate methods based on performance, scalability, and business requirements.

• Manage data acquisition processes by identifying relevant datasets and ensuring data quality. Oversee data preprocessing, cleaning, and transformation to prepare datasets for effective machine learning workflows.

• Perform feature engineering to enhance model performance and improve predictive accuracy. Develop innovative methods for extracting meaningful features from structured and unstructured data sources.

• Train, test, and optimize machine learning models using modern frameworks and tools. Continuously refine models through hyperparameter tuning, experimentation, and performance evaluation techniques.

• Deploy machine learning models into production environments while ensuring reliability, efficiency, and scalability. Collaborate with engineering teams to integrate AI solutions with enterprise systems and applications.

• Implement monitoring systems and validation strategies to track model performance over time. Detect model drift, maintain accuracy, and ensure consistent performance in real-world operational scenarios.

• Research emerging machine learning methodologies and industry best practices. Apply innovative techniques such as deep learning, advanced predictive analytics, and AI automation to enhance business outcomes.

• Mentor junior engineers and data scientists by providing technical guidance and support. Encourage knowledge sharing and help build a culture of innovation, collaboration, and continuous improvement.

• Communicate technical findings, insights, and model outcomes to stakeholders across the organization. Translate complex machine learning concepts into clear and understandable insights for both technical and non-technical audiences.

Requirements & Eligibility

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related technical field. Candidates with strong academic backgrounds in machine learning or statistical modeling are preferred.

• Strong knowledge of machine learning algorithms including regression, classification, clustering, and deep learning techniques. Experience applying these methods to real-world business problems is highly valuable.

• Proficiency in programming languages commonly used for machine learning such as Python or R. Familiarity with ML libraries and frameworks like TensorFlow, PyTorch, Scikit-learn, or similar tools is essential.

• Experience with data preprocessing, feature engineering, and working with large datasets. Ability to clean, transform, and prepare data efficiently for machine learning model development.

• Knowledge of data visualization tools and techniques to present insights clearly. Experience with libraries such as Matplotlib, Seaborn, or other visualization platforms is advantageous.

• Understanding of machine learning lifecycle management including model training, evaluation, deployment, and monitoring. Experience working with production ML systems is a strong advantage.

• Familiarity with cloud platforms, big data technologies, or distributed computing frameworks such as Spark or Hadoop can be beneficial for handling large-scale data processing tasks.

• Strong analytical and problem-solving skills with the ability to design innovative AI solutions for complex challenges. A data-driven mindset and attention to detail are essential.

• Excellent communication and collaboration skills to work effectively with cross-functional teams. Ability to explain technical concepts clearly to business stakeholders is highly valued.

• Demonstrated curiosity for learning new technologies, conducting research, and continuously improving machine learning capabilities within an organization.

Expected Salary

The expected salary for a Machine Learning Engineer at a global technology-driven organization like Mr. Cooper Group typically ranges between ₹12 LPA to ₹25 LPA in India, depending on experience, technical expertise, and the complexity of machine learning projects handled by the candidate. Professionals with strong expertise in AI frameworks, deep learning, and production-grade ML systems may command higher compensation packages along with additional benefits, performance bonuses, and career growth opportunities.

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