AI Research Lead at Avanade leading research combining psychology, data science, and real - world insights. Driving client strategy and organizational transformation through innovative methodologies and analytics.
About the role
- Senior AI Scientist prototyping and implementing AI systems for the education sector. Joining Pearson English Language Learning to develop data-driven educational products.
Responsibilities
- Leading significant parts of work in the lifecycle of development of learning educational capabilities
- Research & development in the field of creating data-driven educational products and solutions
- Designing, implementing and maintaining learning capabilities based on machine-learning and statistics, in collaboration with Engineering teams
- Designing frameworks for monitoring capabilities performance on production (in collaboration with Engineering teams); evaluating results for improvement
- Creating and conducting internal trainings
- Creating technical documentation, incl. patent documentation
- Sharing work results internally and externally (publications, conferences, meetups)
- Staying up to date with state-of-the-art methods in the scope of own specialisation, e.g. NLP, speech processing, statistical modelling, psychometrics
- Providing input into team vision, goals, and processes
- Leading, mentoring and coaching a small group of direct reports (from intern to regular level data scientists)
Requirements
- Experience with generative AI models and techniques.
- Strong collaboration with engineering teams to develop and implement AI solutions.
- Proven experience in building production-ready machine learning models to solve diverse set of business problems.
- Good knowledge of statistical modelling and machine learning theory
- Strong proficiency in Python and machine learning frameworks, e.g. pandas, numpy, scikit-learn, NLTK, spaCy, Hugging Face, TensorFlow, PyTorch
- Proficiency in SQL and relational databases
- Ability to translate business needs into research problems
- Ability to apply best practices for machine learning and software engineering
- Experience with Git-based code review
- Proficiency in Linux, Unix or macOS
- At least intermediate presentation and mentoring skills
- Ability to work in a diverse, remote, asynchronous team
- Collaboration skills, e.g. being able to give (and receive) feedback
- Proficiency in written and spoken English and a key local language
Benefits
- Professional development
- Leading significant parts of work in the lifecycle of development of learning educational capabilities
- Creating internal trainings
- Creating technical documentation
- Sharing work results internally and externally
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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