Roles and Responsibilities
- Performs data analysis and extracts insights out of data using mathematics, statistics, information science, and computer science (including machine learning, statistical learning, and data mining, among others).
- Applies existing technologies, approaches, methodologies in new combinations to design new products, systems or processes. Viewed internally and externally as a specialist in the discipline.
- Presents projects plans, technical roadmaps, risks and recommendations to senior business leaders (EB and SEB) within technical space and occasionally to senior leaders in partner technical teams
- Communicates solutions across the own function and with cross-functional partner organizations.
- Master’s Degree (Ph.D. preferred) in Physics, Engineering, Mathematics, Computer Science or Chemistry from an accredited college or university. Priority will be given to those with physics or applied mathematics training.
- Excellent problem solving skills, demonstrated critical thinking and thirst to learn new areas.
- Deep understanding of machine learning algorithms.
- Minimum 5+ years of advanced analytics development for industrial applications.
- Must have at least 3 years of team leading/mentorship experience.
- Ability to map experience across different domains.
- Strong oral and written communication skills. Strong interpersonal and leadership skills. Demonstated ability to analyze and resolve problems. Demonstrated ability to lead programs / projects. Ability to document, plan, market, and execute programs. Established project management skills.
- Demonstrated skill in the use of one or more analytic software tools or languages (e.g., Python, R, Tensorflow, Pytourch, Caffe, Theano, CNTK, etc.)
- Demonstrated skill in data cleansing, data visualization, data quality assessment, and using analytics for data assessment.
- Demonstrated expertise in modeling and in the development and application of descriptive, applied, and predictive analytics on industrial datasets.
- Experience in machine learning models deployment and life cycle.
- Good understanding of distributed computing and efficient algorithm design.
- Excellent communication (verbal and written) skills with emphasis on business communication.
- Ability to operate with a startup mindset.
- Foster a mentor attitude and be machine learning evangelist within the entire organization.
- Be proactive and take a lead in pushing ideas and PoC forward.