Brainstorm, design and develop the company’s AI initiatives and roadmaps
Formulate the business problem into AI and Machine Learning tasks
Working with different types of data including computer logs, errors, natural languages, and more
Extract, transform, load structured and unstructured text data
Perform AI and machine learning experiments methodically, and conduct data and error analysis to improve models
Document findings, approaches and present the results to the team
Optimize algorithms and prototypical solutions, such as hyperparameter search, model fine-tuning, etc.
Collaborate with the engineering team to implement (build, test and deploy) the ML models
Create unit, integration, end-to-end, and/or performance tests.
Keep current with technology and industry developments and be on the lookout for new approaches and opportunities to integrate them into the existing solutions
Requirements & Skills:
Have strong analytical and problem-solving skills, and be willing to dive deep to find creative solutions.
Have a passion for developing new and innovative solutions with AI, machine learning, and statistical approaches.
Have an entrepreneurial spirit and love to work in a fast-growing start-up environment, where you will meet like-minded people and celebrate successes.
BSc or MSc degree in computer science, engineering, statistics, applied mathematics, or related quantitative discipline.
Ability to synthesize information from multiple sources and act as a thought leaders to design creative solutions.
Experience in ML/AI solution architecture is an asset.
Strong track records in building MLOps and productionizing ML/AI solutions in Azure cloud (or AWS). Familiarity with ML flow is a plus.
Experience in data mining, machine learning, and/or statistical analysis. Knowledge of NLP approaches is an asset.
Strong experience in Python and knowledge of SQL is a plus.
Familiarity with modern machine learning packages such as NumPy, SciPy, Pandas, Matplotlib, seaborn, Scrapy, scikit-learn, etc. Experience with Deep Learning packages such as Pytorch or TensorFlow is an asset.
Knowledge of the Cross Industry Standard Process for Data Mining(CRISP-DM) or similar data-science lifecycle.
Experience building custom integrations between cloud-based systems using APIs.
Experience developing and maintaining ML systems built with open-source tools.
Experience developing with containers and Kubernetes in cloudcomputing environments.
Basic knowledge of agile software development (e.g. versioncontrol, kanban processes, and cloud deployment).
Team player. Comfort working in a dynamic group with open problems to solve.
