Work In Tech

The Role:

As a Senior Data Scientist at Glia, you will focus on applying AI technologies to solve business problems, including supporting the Glia Virtual Assistant (GVA) and its advanced capabilities. As part of the Product organization, you will work side by side with product managers, designers and engineers to decide r on feature feasibility, priorities and design decisions, and will work to ensure key architectural and machine learning principles are taken into account when designing state of the art capabilities in conversational AI. With your technical data science background, you understand both the business and technical needs from different stakeholders. You also collaborate with other parts of the fast-growing company to deploy your work to the real world, and ensure business, quality, and functional goals are met with each release.

What you’ll do:

• Assess value, develop use cases, and prioritize stories, epics and initiatives to ensure work focuses on those with maximum value that are aligned with product strategy
• Research, design, implement and deploy conversational AI related components in the product, including models, config and data.
• Design and implement internal tools, systems and pipelines for different Phases of Data Science / Natural Language Understanding projects as required
• Help with the designing of our model infrastructure including how entity, intent and dialogue models are designed and integrated
• Build, maintain and improve NLU AI model performance, and tooling to support incremental updates to models in terms of multi-turn conversation, additional understanding capabilities and contextual improvements
• Design, configure and develop machine learning algorithms for detecting patterns in user behavior
• Conduct research in the field, present and summarize findings from academic and industry literature and engage in continuous learning

Requirements:

• Masters degree or equivalent achievement in computer science, artificial intelligence or data science. Thesis based graduate experience is preferred.
• Minimum 3 years of data science industry experience
• Great team player contributing to create good team culture
• Strong experience in research and analysis, clearly identify and communicate findings and recommendations
• Excellent verbal and technical writing skills to document and present your ideas to both technical and non-technical stakeholders
• Strong technical and previous hands-on experience with AI deep learning, specifically in NLP, and preferably including Transformer architectures
• Experienced in common technologies used by data science team, such as Python, SQL, Jutyper notebooks; and experience with deep learning theory and application using mxnet, Tensor flow or other deep learning frameworks
• Experience using large language models (LLMs) such as BERT
• Experience with supervised and unsupervised deep learning systems, theories and frameworks. This could include text classification, entity recognition, image recognition, machine translation, recommender systems, auto-encoders, etc.
• Strong experience in agile methodologies, processes, software engineering practices, design patterns and common systems architecture

Bonus Experience:

• Experience with AI based virtual assistants or chatbots
• Experience working with / in the banking, insurance or credit union industry
• Experience with data analysis and reporting, familiar with analytic tools such as Tableau