Applied Scientist

1 week ago

Gurugram Haryana, India ADCI - Haryana Full time

Bachelors or Masters in Computer Science, Mathematics or related field, or equivalent experience with 1-3 years of industry work experience in related field
- 1-3 years of hands-on experience building ML models and deploying them to production
- A strong ability in understanding statistical models and analyze them to provide rigorous solutions for business needs.
- Computer Science fundamentals in data structures, algorithm design, problem solving, and complexity analysis
- Proficiency in, at least, one modern programming language such as C, C++, Java, or Perl
- Proficiency in Python, R, MATLAB or similar scripting language

Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network.
Core Transportation Technology is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Core Trans, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting off-manifest at a package level. Your models will help optimize shipping cost by improving quality of upstream data (such as through corrections to weights and dimensions in product catalog). Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications.
You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services.
You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences.
If you are excited by this charter, come join us
- Significant peer reviewed scientific contributions in relevant field
- Expertise on a broad set of ML approaches and techniques
- Prior Experience in Transportation Logistics business
- Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts

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