Machine Learning Intern

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:

1. Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
2. Managing available resources such as hardware, data, and personnel so that deadlines are met
3. Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
4. 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
5. Verifying data quality, and/or ensuring it via data cleaning
6. Supervising the data acquisition process if more data is needed
7. Finding available datasets online that could be used for training
8. Defining validation strategies
9. Defining the preprocessing or feature engineering to be done on a given dataset
10. Defining data augmentation pipelines
11. Training models and tuning their hyperparameters
12. Analyzing the errors of the model and designing strategies to overcome them
13. Deploying models to production
14. Knowledge and understanding of GCP Ecosystem

Qualifications:

1. 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
2. 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
3. Strong, proven programming skills with machine learning and deep learning, and Big data frameworks including TensorFlow, Caffe, Spark, Hadoop
4. Experience with writing complex programs and implementing custom algorithms in these and other environments leveraging languages like Python, PySpark
5. Experience beyond using open-source tools as-is, and writing custom code on top of, or in addition to, existing open-source frameworks
6. 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
7. Research and implement novel machine learning and statistical approaches
8. Experience in data management, data analytics middleware, platforms, and infrastructure, cloud and fog computing is a plus
9. 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