Bell’s forward-thinking Customer Operations team is creating the ultimate service experience for our residential, wireless and small business consumers. We lead strategic development and execution of day-to-day operations, develop tools and processes to drive service enhancements, manage customer loyalty and retention, and leverage big data and artificial intelligence to create intellectual property.
 This is a terrific opportunity for a seasoned professional to help transform the organization in driving one of the most significant strategic mandates within Bell.  As part of the Customer Operations - Data Science team, you will have opportunities to use your data science and leadership skills to shape the next generation of AI/ML to truly transform how Bell operates.

 The Bell Speech Technologies team is an industry leader with deep expertise in automatic speech recognition and speech analytics.  You will be working with a world-class team of peers to build products and services that deliver on the promise of AI by solving complex, high-value business problems.  You will work in a highly collaborative environment, and use Agile methodologies to understand, develop, and iterate on solutions that meet customer needs.  Access to cutting edge speech to text and natural language tools, all within a cloud environment.

 Above all, we are looking for team members with a keen sense of ownership, resilience and drive. If you like dynamic projects and enjoy being challenged, if you can make progress when faced with ambiguous requirements and uncertainty, if you enjoy moving fast (and are not afraid of making a mistake or two on the way), then we may be the perfect team for you!

 Job Duties/ Accountabilities:

• Lead and implement speech technologies tools and models
• Lead the development of natural language machine learning products/ models from inception to production
• Your team’s work will span requirements gathering & architecture, coding, testing, and deploying complex machine learning-based systems
• Leading cross functional AI/ML teams of Data Scientists, Analysts, and Labelers
• Support a team of approximately 15 employees

 Critical Qualifications/ Competencies:

• Practical experience in NLP
• Experience in developing some of the following NLP models: Text summarization, text generation, sentiment and topic analysis, named-entity recognition, etc
• Ability to lead NLP projects
• Hands-on experience defining and operationalizing ML models into operational systems
• Degree in a discipline such as: Data Science, applied math, applied science / engineering, economics/econometrics, management science / operations research, or related area

• 2+ years experience in strategy, planning, consulting, analytics or related disciplines
• 2+ years of direct people management experience
• A successful track record as a leader, developer and manager of people; demonstrated experience to attract, retain and develop strong talent and build a positive organization and team culture
• Experience working closely with senior VP level executives across multiple business units
• Self Motivation and drive to deliver high-quality solutions
• Strong interpersonal skills, including the ability to collaborate across the team and work closely with stakeholders
• Excellent written and oral communication skills (English) with a demonstrable history of developing strong partnerships and cross-team collaboration
• Strong business acumen, with an appreciation for the opportunities and constraints that accompany an ever-evolving competitive environment
• Proven project management and change management skills

Preferred Qualifications:

ML/NLP technical expertise (frameworks and libraries):

• GCP Vertex AI Platform
• Python development skills
• Practical experience with NLP frameworks/libraries e.g. Gensim, SpaCy
• Experience with general ML/Data Science tools in Python: numpy, pandas, sklearn, jupyter etc
• Hands-on experience and expertise with different AI/ML frameworks such as Keras, Pytorch, TensorFlow, SparkML, Scikit-Learn
• Ontology graphs, information retrieval, Elastic search
• Computer science fundamentals
• Database: SQL, Hadoop

 Theoretical knowledge:

• Advanced knowledge of ML models:  deep learning, reinforcement learning, and others
• Theoretical understanding of neural networks, backpropagation, and optimization algorithms with emphasis on mathematical understanding.
• Mathematical understanding of some ML algorithms: regression, SVM, decision trees, etc…
• Experience with word embedding’s and or embedding quality evaluation