Job
Description
The Purpose of the role We are seeking a full stack data scientist in Advanced Analytics team, who will be at the forefront of developing new innovative data driven solutions with bleeding edge machine learning and AI solution end to end. AIML Data Scientist is a technical job that uses AI & machine learning techniques to automate underwriting processes, improve claims outcomes and/or risk solutions. This person will develop vibrant data science solutions which require data engineering, AlML algorithms and Ops engineering skills to develop and deploy it for the business. Ideal candidate for this role is someone with a strong education in computer science, data science, statistics, applied math or a related field, and who is eager to tackle problems with innovative thinking without compromising detail business insights. You are adept at solving diverse problems by utilizing a variety of different tools, strategies, machine learning techniques, algorithms and programming languages. Responsibilities Work with business partners globally, determine analyses to be performed, manage deliverables against timelines, present of results and implement the model. Use broad spectrum of Machine Learning, text and image AI models to extract impactful features from structured/unstructured data. Develop and implement models that help with automating, getting insights, make smart decisions; Ensure that the model is able to meet the desired KPIs post-production. Develop and deploy scalable and efficient machine learning models. Package and publish codes and solutions in reusable format python package format- (Pypi, Scikit-learn pipeline,..) Keep the code ready for seamless building of CI/CD pipelines and workflows for machine learning applications. Ensure high quality code that meets business objectives, quality standards and secure web development guidelines. Building reusable tools to streamline the modeling pipeline and sharing knowledge Build real-time monitoring and alerting systems for machine learning systems. Develop and maintain automated testing and validation infrastructure. Troubleshoot pipelines across multiple touchpoints like CI Server, Artifact storage and Deployment cluster. Implement best practices for versioning, monitoring and reusability. Requirements Sound understanding of ML concepts, Supervised / Unsupervised Learning, Ensemble Techniques, Hyperparameter Good knowledge of Random Forest, XGBoost, SVM, Clustering, building data pipelines in Azure/Databricks, deep learning models, OpenCV, Bert and new transformer models for NLU, LLM application in ML> Strong experience with Azure cloud computing and containerization technologies (like Docker, Kubernetes). 4-6 years of experience in delivery end to end data science models. Experience with Python/OOPs programming languages and data science frameworks like (Pandas, Numpy, TensorFlow, Keras, PyTorch, sklearn). Knowledge of DevOps tools such as Git, Jenkins, Sonar, Nexus is must. Building python wheels and debugging build process. Data pipeline building and debugging (by creating and following log traces). Basic knowledge of DevOps practices. Concepts of Unit Testing and Test-Driven development. SDE skills like OOP and Functional programming are an added advantage. Experience with Databricks and its ecosystem is an added advantage. analytics/statistics/mathematics or related domain. Show more Show less
