Data Science Software Engineer

As a Machine Learning Engineer, you will be responsible for developing and implementing machine learning algorithms and techniques to solve business problems and enhance member experiences. Your primary duties will include: - Designing machine learning projects to address specific business problems in consultation with business partners. - Working with data sets of varying sizes and complexities, including both structured and unstructured data. - Processing massive data streams in distributed computing environments like Hadoop to facilitate analysis. - Implementing batch and real-time model scoring to drive actions. - Developing machine learning algorithms for customized solutions that go beyond standard industry tools, leading to innovative solutions. - Creating sophisticated visualizations of analysis output for business users. To qualify for this role, you should have: - A degree in Statistics, Computer Science, Mathematics, Machine Learning, Econometrics, Physics, Biostatistics, or related quantitative disciplines (BS/MA/MS/PhD). - 2-4 years of experience in predictive analytics and advanced expertise with software such as Python or equivalent combination of education and experience. - Experience in the healthcare sector. - Strong preference for experience in Deep Learning. Your technical skill set should include: - Understanding a wide range of algorithms and their corresponding problem-solving applications. - Data preparation and analysis. - Model training and validation. - Model application to specific problems. - Working with full open-source programming tools and utilities. - End-to-end data science project implementation. - Development and deployment of Machine Learning applications. - NLP approaches in a production setting. - Building models using bagging and boosting algorithms. - Experience in building Deep Learning models for NLP/Computer Vision use cases. - Writing efficient code with a good understanding of core Data Structures/algorithms. - Strong Python skills following software engineering best practices. - Using code versioning tools like GIT, Bitbucket. - Working in Agile projects. - Comfort and familiarity with SQL and the Hadoop ecosystem of tools including Spark. - Managing big data with efficient query programming. - Training ML models in tools like Sage Maker, Kubeflow, etc. - Using frameworks to depict interpretability of models using libraries like Lime, Shap. - Experience in the healthcare sector is preferred. - An MS/M.Tech or PhD is a plus. As a Machine Learning Engineer, you will be responsible for developing and implementing machine learning algorithms and techniques to solve business problems and enhance member experiences. Your primary duties will include: - Designing machine learning projects to address specific business problems in consultation with business partners. - Working with data sets of varying sizes and complexities, including both structured and unstructured data. - Processing massive data streams in distributed computing environments like Hadoop to facilitate analysis. - Implementing batch and real-time model scoring to drive actions. - Developing machine learning algorithms for customized solutions that go beyond standard industry tools, leading to innovative solutions. - Creating sophisticated visualizations of analysis output for business users. To qualify for this role, you should have: - A degree in Statistics, Computer Science, Mathematics, Machine Learning, Econometrics, Physics, Biostatistics, or related quantitative disciplines (BS/MA/MS/PhD). - 2-4 years of experience in predictive analytics and advanced expertise with software such as Python or equivalent combination of education and experience. - Experience in the healthcare sector. - Strong preference for experience in Deep Learning. Your technical skill set should include: - Understanding a wide range of algorithms and their corresponding problem-solving applications. - Data preparation and analysis. - Model training and validation. - Model application to specific problems. - Working with full open-source programming tools and utilities. - End-to-end data science project implementation. - Development and deployment of Machine Learning applications. - NLP approaches in a production setting. - Building models using bagging and boosting algorithms. - Experience in building Deep Learning models for NLP/Computer Vision use cases. - Writing efficient code with a good understanding of core Data Structures/algorithms. - Strong Python skills following software engineering best practices. - Using code versioning tools like GIT, Bitbucket. - Working in Agile projects. - Comfort and familiarity with SQL and the Hadoop ecosystem of tools including Spark. - Managing big data with efficient query programming. - Training ML models in tools like Sage Maker, Kubeflow, etc. - Using frameworks to depict interpretability of models using libraries like Lime, Shap. - Experience in the healthcare sector is pr