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No other bank is doing what we do.
At BDC, we’re devoted to Canadian entrepreneurs. We’re also dedicated to our employees. Adaptable. Inspiring. Different. There’s a reason we like to work here and we think you’ll like it too.
BDC is looking for bright, knowledgeable and motivated Senior Data Scientists experienced with the state-of-the-art (SOTA) Machine Learning (ML), Deep Learning (DL), and big data architectures.
As a Senior Data Scientist, you will be working within the AAAI team and contribute to enterprise-wide best practices, processes
The bulk of the work will be in framing the problem, data exploration and preparation, statistical hypothesis testing, feature engineering, modelling, model fine-tuning, storytelling with data, operationalization and model tracking.
Responsibilities / Accountabilities
• Accessing, profiling, and analyzing various relational databases, datalake (HDFS), unstructured data (files, images), NoSQL or graph databases
• Applying statistical analysis and visualization techniques to various data, such as hierarchical clustering, t-distributed Stochastic Neighbor Embedding (t-SNE), LLE, EM, Ward, and PCA
• Generating hypotheses about the underlying mechanics of the business process
• Testing hypotheses using various quantitative methods
• Networking with business subject matter experts, product owners and product managers to better understand the business mechanics that generated the data
• Analysis of the data through descriptive, exploratory, inferential, and causal techniques
• Preparation of the data through standardization, normalization, imputation, cleansing, outlier detection, and formatting
• Engineering features for continuous and discrete data utilizing domain knowledge and statistical techniques
• Applying various ML, DL, Reinforcement Learning (RL) and Advanced Analytics techniques to create supervised, semi-supervised, self-supervised, and unsupervised models
• Evaluation and testing of AI models through cross-validation, A/B testing, bias and fairness evaluation, and Explainability / interpretability
• Utilizing SOTA methods in ML and DL to achieve the superior model performance
• Implementing novel deep learning architectures and employing the Neural Architecture Search (NAS), and Meta Learning techniques
• Collaboration with the Data Product Manager, Data Analysts, other Data Scientists, MLOps Engineers, ML Engineers, and Data Engineers to evaluate, implement, and deploy to production enterprise-grade Machine Learning and Deep Learning models
Requirements
• A master’s degree in Computer Science, Statistics, Mathematics or related fields
• A Ph.D. degree in Computer Science, Statistics, Mathematics or related fields is preferred
• Working in an academic AI research lab is a plus
• Academic publications on Deep Learning, Machine Learning or Operations Research is a plus
• 5+ years of industry experience as a Data Scientist
• 5+ years of experience with Python programming language
• 3+ years of experience with scalable production grade Data Science (e.g. lifecycle management, experimentation management, model telemetry, and registry)
• 3+ years of experience with Scikit-Learn, Pandas, Seaborn, Numpy, Scipy, LightGBM, AdaBoost, CatBoost, and XGBoost
• 2+ years of experience with Keras, Tensorflow and Pytorch for Deep Learning
• Experience with Big Data, Lambda Architectures (Batch & Stream processing) and visualization
• Experience of working in banks and financial institutions (FinTech experience is a plus)
• Experience with Scala programming language is a plus
• Experience with Apache Kafka for Event Streaming (Confluent platform knowledge is a plus)
• Experience with Apache Spark (Databricks platform knowledge is a plus)
• Knowledge of ML Engineering and MLOps
• Knowledge of Graph databases (JanusGraph, Apache TinkerPop or Gremlin)
• Experience with agile processes and Software Engineering best practices
• Strong interpersonal, teamwork, coordination and consensus building skills
• Strong communication, documentation, storytelling, creativity, and presentation skills
• Strong organizational skills, the ability to perform under pressure and to manage multiple priorities with competing demands

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