Machine Learning Intern
2 months ago
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 progress
- Managing available resources such as hardware, data, and personnel so that deadlines are met
- Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
- Exploring 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 world
- Verifying data quality, and/or ensuring it via data cleaning
- Supervising the data acquisition process if more data is needed
- Finding available datasets online that could be used for training
- Defining validation strategies
- Defining the preprocessing or feature engineering to be done on a given dataset
- Defining data augmentation pipelines
- Training models and tuning their hyperparameters
- Analyzing the errors of the model and designing strategies to overcome them
- Deploying models to production
- Knowledge and understanding of GCP Ecosystem
Qualifications:
- 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 & modeling
- 0-1 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 plus
- Strong, proven programming skills with machine learning and deep learning, and Big data frameworks including TensorFlow, Caffe, Spark, Hadoop
- Experience with writing complex programs and implementing custom algorithms in these and other environments leveraging languages like Python, PySpark
- Experience beyond using open-source tools as-is, and writing custom code on top of, or in addition to, existing open-source frameworks
- Proven 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 domains
- Research and implement novel machine learning and statistical approaches
- Experience in data management, data analytics middleware, platforms, and infrastructure, cloud and fog computing is a plus
- Excellent communication skills (oral and written) to explain complex algorithms, and solutions to stakeholders across multiple disciplines, and ability to work in a diverse team
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