Director Data Science

Key Skills

Algorithms, CNNs, Data Modeling, Data science techniques, Deep Learning, Design, GRU4Rec, Java Programming, Machine learning techniques, Management, MLOps tools, Python Programming, PyTorch, Random forests, RNNs, SQL, TensorFlow

Job Description

RBC Technology Infrastructure seeks a Principal/Director of Data Science to develop, design, and implement leading AIOps (AI for IT Operations) solutions. By leveraging leading-edge technologies and various data sources, you will develop machine learning-based services that will facilitate informed decision-making and business process optimization that will reduce outages and reduce downtime for the RBC services, thus saving significant amounts of money and improving user experience.

What will you do?

• Work on challenging and research-based initiatives using advanced machine learning methods focusing on tangible outcomes
• Partner with the product manager, technology teams, and various business and operation units to identify opportunities, conduct workshops and translate them into a data science roadmap
• Understand business issues and decompose them into measurable data or modeling requirements. Analyze data from different sources and provide key insights throughout the strategy, design, development, and deployment stages
• Collaborate with product and engineering teams to define and execute against the product roadmap that supports the business objectives
• Coach and build AI models leveraging advanced ML/DL techniques
• Perform model tuning and frequent training to maintain proper health of ML models by ingesting newer data sources, reducing errors in ML systems, and applying end-to-end learning, transfer learning, and multi-task learning
• Produce data-driven insights to help in informed decisions and actions by telling a convincing story and effectively communicating the findings to business partners and executives
• Quickly learn new methods, tools, and technologies presented in research communities to implement and adapt within the daily analytics exercises

What do you need to succeed?

Must-have

• Master’s degree or Ph.D. in a technology/analytical field such as Computer Science, Management of Information Systems, Data Science, Machine Learning, Engineering, or other relevant technology fields
• A minimum of seven years of relevant professional experience in an Agile environment with an excellent understanding of the underlying Statistical, Machine Learning theory, and Predictive Modeling Lifecycle
• Deep expertise in developing and maintaining predictive models using advanced ML/DL techniques. Proficiency in building Supervised learning models [ (classification and regression) – tree-based (Random Forest, Stochastic Gradient Boosting and eXtreme Gradient Boosting, Decision Trees, Extra Trees, Regularized Greedy Forests), Generalized Linear Models, Discriminant models (LDA, MDA, FDA and QDA)], and Unsupervised learning models (Isolation Forest, Clustering algorithms)
• A minimum of three years of experience in developing or mentoring machine learning teams
• Experience in building Deep neural networks (MLP, CNN, RNN, GRUs, and LSTMs) and use of AI/Deep Learning frameworks like TensorFlow, PyTorch, CNTK, and Keras is an added advantage
• Experience with MLOps to build end-to-end pipelines and deploy models in production
• Exceptional interpersonal, persuasion, and communication skills to communicate strategic and technical ideas to internal audiences to both inform and solicit buy-in from the end-user community

Nice-to-have

• Experience working with GPT 3 Models
• Exposure to Reinforcement learning (DynaQ/Q+, SARSA, TD, Monte Carlo)
• Experience developing Anomaly detection and recommender engines solutions