Develops, designs, adapts, prototypes and implements in code, supervised and unsupervised machine learning and advanced statistical algorithms.  Evaluates and recommends approaches to various data analytics problems. Analyzes and evaluates data sets for both insight into the data and usability for machine learning applications.  Conditions data for analysis and works with large scale structured and unstructured data.  Creates data pipelines and works with large scale, distributed compute and storage platforms. Completes programming and implements efficiencies, performs testing and debugging. Completes documentation and procedures for installation and maintenance.

• Works on assignments with broadly defined objective
• Solves straightforward issues, challenges and problems within field of specialization
• Requires general proficiency with tools, systems and procedures to accomplish job.

• Bachelors or Masters Degree or equivalent and 2-4 years relevant experience.
• Proven experience as a Machine Learning Engineer or similar role.
• Understanding of data structures, data modeling and software architecture.
• Deep knowledge of math, probability, statistics and algorithms.
• Ability to write robust code in Python
• Experience in developing middle-tier and backend components in enterprise applications 
• Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn).
• Familiarity with 3GPP cellular network standards
• Practical experience with planning, measurement, optimization or troubleshooting of 3GPP cellular networks.

• Study and transform data science prototypes.
• Research and implement appropriate ML algorithms and tools.
• Select appropriate datasets and data representation methods.
• Run machine learning tests and experiments.
• Perform statistical analysis and fine-tuning using test results.
• Analyze the ML algorithms that could be used to solve a given problem and rank them by their success probability.
• Explore and visualize data to gain an understanding of it.
• Identify differences in data distribution that could affect performance when deploying the model in production.
• Train and retrain systems when necessary.
• Extend existing ML libraries and frameworks.
• Keep abreast of developments in the field.