As an Machine Learning Intern, you will help develop and enhance the algorithms and technology that powers our unique system. This role covers a wide range of challenges, from developing new models using pre-existing components to enable current systems to be more intelligent.
Roles and Responsibilites:
Understanding business objectives and developing models that help to achieve them, along with metrics to track their progressManaging available resources such as hardware, data, and personnel so that deadlines are metAnalyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probabilityExploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real worldVerifying data quality, and/or ensuring it via data cleaningSupervising the data acquisition process if more data is neededFinding available datasets online that could be used for trainingDefining validation strategiesDefining the preprocessing or feature engineering to be done on a given datasetDefining data augmentation pipelinesTraining models and tuning their hyperparametersAnalyzing the errors of the model and designing strategies to overcome themDeploying models to productionKnowledge and understanding of GCP EcosystemQualifications:
BE/B.Tech/Masters in a quantitative field such as CS, EE, Information sciences, Statistics, Mathematics or related, with a focus on applied and foundational Machine Learning, AI, NLP and/or/data-driven statistical analysis & modeling0-1 years of Experience majorly in applying AI/ML/NLP/deep learning / data-driven statistical analysis & modeling solutions to multiple domains, including financial engineering, financial processes a plusStrong, proven programming skills with machine learning and deep learning, and Big data frameworks including TensorFlow, Caffe, Spark, HadoopExperience with writing complex programs and implementing custom algorithms in these and other environments leveraging languages like Python, PySparkExperience beyond using open-source tools as-is, and writing custom code on top of, or in addition to, existing open-source frameworksProven capability in demonstrating successful advanced technology solutions (either prototypes, POCs, well-cited research publications, and/or products) using ML/AI/NLP/data science in one or more domainsResearch and implement novel machine learning and statistical approachesExperience in data management, data analytics middleware, platforms, and infrastructure, cloud and fog computing is a plusExcellent communication skills (oral and written) to explain complex algorithms, and solutions to stakeholders across multiple disciplines, and ability to work in a diverse team
