Responsibilities:

📍Design, develop, and implement machine learning models using supervised and unsupervised learning techniques, deep learning, and reinforcement learning. 📍Proficiently apply statistical algorithms and model evaluation techniques to ensure model accuracy and performance. 📍Utilize frameworks such as PyTorch, TensorFlow, and Scikit-learn to build and optimize machine learning solutions. 📍Perform large-scale data wrangling and transformation to prepare data for modeling and analysis. 📍Collaborate with software engineering teams to integrate machine learning models into production systems, adhering to software design principles. 📍Demonstrate programming proficiency in Python and Pyspark, ensuring clean and efficient code. 📍Utilize version control systems such as Git to manage codebase and collaborate with team members. 📍Understand and implement APIs and microservices to enable seamless integration of machine learning solutions. 📍Stay updated with the latest advancements in machine learning and financial services, applying new knowledge to improve existing models and processes.

Requirements:

📍Experience Range: 7+ Years 📍Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field. 📍Proven experience in machine learning and statistical algorithms, with a strong portfolio of projects. 📍Proficiency in machine learning frameworks, including PyTorch, TensorFlow, and Scikit-learn. 📍Strong programming skills in Python and Pyspark, with a solid understanding of software engineering practices. 📍Experience with data wrangling, transformation, and feature engineering for large datasets. 📍Familiarity with version control systems (e.g., Git) and understanding of API and microservices architecture. 📍Excellent problem-solving skills, attention to detail, and the ability to work collaboratively in a fast-paced environment.

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