Job
Description
Responsibilities: - Contribute and build an internal product library that is focused on solving business problems related to prediction & recommendation. Research unfamiliar methodologies, techniques to fine tune existing models in the product suite and, recommend better solutions and/or technologies. Improve features of the product to include newer machine learning algorithms in the likes of product recommendation, real time predictions, fraud detection, offer personalization etc Collaborate with client teams to on-board data, build models and score predictions. Participate in building automations and standalone applications around machine learning algorithms to enable a One Click solution to getting predictions and recommendations. Analyze large datasets, perform data wrangling operations, apply statistical treatments to filter and fine tune input data, engineer new features and eventually aid the process of building machine learning models. Run test cases to tune existing models for performance, check criteria and define thresholds for success by scaling the input data to multifold. Demonstrate a basic understanding of different machine learning concepts such as Regression, Classification, Matrix Factorization, K-fold Validations and different algorithms such as Decision Trees, Random Forrest, K-means clustering. Demonstrate working knowledge and contribute to building models using deep learning techniques, ensuring robust, scalable and high-performance solutions Minimum Qualifications: Education: Master's or PhD in a quantitative discipline (Statistics, Economics, Mathematics, Computer Science) is highly preferred. Deep Learning Mastery: Extensive experience with deep learning frameworks (TensorFlow, PyTorch, or Keras) and advanced deep learning projects across various domains, with a focus on multimodal data applications. Generative AI Expertise: Proven experience with generative AI models and techniques, such as RAG, VAEs, Transformers, and applications at scale in content creation or data augmentation. Programming and Big Data: Expert-level proficiency in Python and big data/cloud technologies (Databricks and Spark) with a minimum of 4-5 years of experience . Recommender Systems and Real-time Predictions: Expertise in developing sophisticated recommender systems, including the application of real-time prediction frameworks. Machine Learning Algorithms: In-depth experience with complex algorithms such as logistic regression, random forest, XGBoost, advanced neural networks, and ensemble methods. Experienced with machine learning algorithms such as logistic regression, random forest, XG boost, KNN, SVM, neural network, linear regression, lasso regression and k-means. Desirable Qualifications: Generative AI Tools Knowledge: Proficiency with tools and platforms for generative AI (such as OpenAI, Hugging Face Transformers). Databricks and Unity Catalog: Experience leveraging Databricks and Unity Catalog for robust data management, model deployment, and tracking. Working experience in CI/CD tools such as GIT & BitBucket
