Data Scientist (Machine Learning)

Responsibilities:

Analyzes business critical data and recommends improvements. 
Works with large data sets consisting of predominantly images and conducts advanced analytics tasks. Assess the effectiveness and accuracy of new data sources and data gathering techniques. 
Works with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions. 
Develops custom data models and algorithms to apply to data sets. 
Coordinates with different functional teams to implement models and monitor outcomes. 
Develops processes and tools to monitor and analyze model performance and data accuracy. 
Serves as internal consultant to other developers and engineers as needed, providing assistance in all phases of product life-cycle development. 
Advises developers and engineers on latest data analytics technologies and assists the team in process matters as related to development/support and provides the necessary on the job training and development of associates/contractors within the team. 
Maintains accurate, meaningful and updated technical and non-technical documentation pertaining to all aspects of area(s) of responsibility. 
Performs other duties as assigned Supervisor

Qualifications:

Bachelor degree in computer science, mathematics/statistics or related field. 
Advanced degree (Masters or PhD) in computer science, mathematics/statistics or a related field (preferred). 
3+ years post-university experience in advanced analytics in the field of data science, applying scientific data analytics methods preferably to automotive industry datasets. 
1+ years proven experience completing projects with a set of various data sources. 
3+ years of knowledge in using statistical computer languages (Python, SLQL, etc) to manipulate data and draw insights from large data sets
1+ years of knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks. 
Intermediate knowledge of deep learning architectures (RNN, CNN, LSTM, etc.) and frameworks (Tensor flow, Keras, etc.)
3+ years of knowledge of in one or more of the following programming languages: Python, Java, C++
3+ years of knowledge and experience in Computer Science and in database technologies including SQL, Oracle, SQLServer, SAP HANA and NoSQL databases
3+ years of experience in statistical languages and tools in particular R
3+ years of experience in problem solving skills with an emphasis on product development 
AWS (Sagemaker, Athena, Glue)
Machine Learning Experience