Yerevan, Armenia
START DATE: ASAP
APPLICATION DEADLINE: December 15, 2018
We are seeking a creative, motivated principal data scientist/AI/ML specialist with diverse experiences and interests in data science/AI/ML, and the ability to adapt skill set to a variety of applications. The role requires good knowledge and understanding of the field. This is an exciting and interdisciplinary role for a highly qualified and motivated individual, who will be responsible for mentoring and supervising research associates. We are looking for an enthusiastic, innovative scientist with a passion for generating and bringing novel ideas to markets. S(he) will be comfortable working in and contributing to a very dynamic and cross-functional team environment. The applicant will be a crucial part of a fast-paced data scientists/AI team.
Key responsibilities:
- Ph.D. in data science, computer science, statistics, mathematics, physics or a related field with 5+ years of experience
- Proficiency in relevant programming/scripting languages such as Python or R and standard command line tools
- In depth experience in one or more of the following areas: natural language processing, speech recognition, voice recognition, image processing, expert systems, or a related field
- Expertise in one or more of the core machine learning areas such as: ANNs, SVMs, reinforcement learning, probabilistic rule-based learning
- Experience in algorithm generation and/or computational method implementation
- Demonstrated experience with large-scale data and high throughput data analysis, network analysis and meta analyses.
Basic Qualifications:
- PhD in data science, computer science, statistics, mathematics, physics or an adjacent field
- Expertise in one or more of the core machine learning areas such as: ANNs, SVMs, reinforcement learning, probabilistic rule-based learning
- Experience with relevant software tools such as R and Python as well as relevant machine learning frameworks such as scikit-learn and tensorflow
- Experience in algorithm generation and/or computational method implementation
- Highly adaptable and responsive to technical and business opportunities and needs – comfortable delivering against challenging commitments.
Preferred Qualifications:
- Demonstrated experience with large-scale data and high throughput data analysis, network analysis and meta analyses
- 3+ years of experience
- Expertise in one or more of such areas as: Bayesian approaches, Markov models, Gaussian processes, game theory, decision theory
- Excellent communication and presentation skills, capable of conveying technical information in a clear and thorough manner
- Team oriented, highly motivated, execution focused with strong work ethic, ability to thrive in an entrepreneurial and multidisciplinary environment
Application procedure:
All interested candidates are kindly requested to submit their CVs to hr@fast.foundation.
Please indicate the title of the position “Principal” in the subject line of your e-mail. FAST is grateful to all interested applicants. All applicants will be notified of their application results.
About FAST:
Foundation for Armenian Science and Technology (FAST) is conceived as a comprehensive platform to foster technological and scientific breakthroughs in Armenia. FAST aims to revive the country’s strong Science, Technology, Engineering and Mathematics (STEM) traditions, establishing a favorable regime for the innovative environment, fostering science and technology education, and actively developing a venture capital ecosystem. One of the most pivotal tasks of FAST is to engage tech and scientific communities in raising the profile of Armenia as a hub for technological and scientific discoveries and developments.
Application deadline:
December 15, 2018
Note: Employment decisions are made without regard to race, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability or other characteristics protected by law.
This opportunity has expired. It was originally published here:
https://fast.foundation/#/ex-ar-principal