Applied Machine Learning Scientist I ATH# 5645 (Tabular Deep Learning Model Validation)
$105.5k - $134.5k per yearTD
Work Location: Toronto, Ontario, Canada Hours: 37.5 Line of Business: Analytics, Insights, & Artificial Intelligence Pay Details: $105,500 - $134,500 CAD The pay details posted reflect a temporary market premium specific to this role that is reassessed annually. TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs. As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role. Job Description: Department Overview TD Model Validation (MV) group is responsible for the independent validation and approval of models used for Generative AI, Natural Language Processing (NLP), Tabular Deep Learning, credit, fraud, and marketing models. The Artificial Intelligence/Machine Learning (AI/ML) MV team is responsible for the validation of all AI/ML models used across the Bank for various use cases. Job Description The position reports to Senior Applied Machine Learning Scientist in the AI/ML Model Validation team and is primarily focused on the validation and review of Deep Learning models. Detailed accountabilities include:
- Research, validate, and apply new techniques in testing deep learning models applied to tabular/structured datasets.
- Stay up to date with advancements in the field of AI including major publications, foundation models, evaluation metrics, explainability, technology stacks, and datasets.
- In depth knowledge of techniques and developments in the field of AI/ML and share knowledge with business partners and senior management.
- Develop/implement AI/ML model validation methodologies and standards, especially for Tabular Deep Learning / Foundation models. Ensure that the validation methodologies and standards are in line with industry best practices and address regulatory and audit requirements.
- Work in Distributed Cluster / Cloud environments with large-scale datasets ranging from transactions to large document collections.
- Develop and apply a variety of statistical tests and modeling techniques to identify/recommend improvements to models and undertake related initiatives. Implement benchmark models as applicable.
- Conduct R&D in the area of Tabular Deep Learning and Foundation Models evaluation, testing, explainability.
- Communicate findings and recommendations to both technical and non-technical stakeholders.
- Strong quantitative skills with an advanced degree in one or more of the following areas: computer science, machine learning / AI, engineering, statistics, mathematics, or physics.
- Experience with and strong knowledge of AI/ML methodologies including Deep Learning, modern Natural Language Processing (NLP), Transformers, Diffusion models, and Bagging/Boosting methods.
- Experience with Deep Learning technology stacks and libraries such as PyTorch, Tensorflow/Keras etc.
- Stay up to date with the latest advancements in Tabular Deep Learning / Foundation models, Machine Learning, and Cloud technologies.
- Proficient in one or more scripting/programming languages such as Python, Java, Scala, Pyspark.
- Familiarity with cloud platforms (e.g., Azure Databricks, Azure ML Studio).
- Familiarity with Data Structures, Algorithm design, and principles of Object-Oriented Programming (OOP).
- Knowledge of machine learning explain-ability/interpretability algorithms.
- Excellent verbal and written communication skills. The position requires writing clean technical reports.
- Ability to work independently and collaboratively in a fast-paced, dynamic environment. Great time management and multitasking skills with minimal supervision.
- Publications in the relevant conference and journals are a plus.
- Ability to implement AI/ML algorithms from academic research papers is a plus.
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