## Engage with stakeholders to understand business requirements and objectives of ML / AI use cases
## Identify and acquire the best data sources available to meet use case objectives. Explore data to build foundational data knowledge in subject domain, in partnership with data owners
## Develop cutting-edge ML models tailored to identified use cases and ensure the accuracy, reliability, and innovation of these models is crucial for making impactful contributions to our data-driven initiatives.
## Collaborate closely with Data and MLOps Engineers, to implement the models in the Big Data Platform, leveraging cloud technologies.
## Present technical analyses in a meaningful and intuitive manner, in accordance with the target audience
## For each use case, thoroughly document the objectives, analysis process, methods, and findings to meet quality standards and production requirements
## Continuously monitor and fine tune model performance by evaluating new or revised data sources, modelling techniques, and tools
## Identify, evaluate, and manage external vendors for specialized skills
## Actively partner with the Data Governance team to ensure that the use of data for each use case complies with Teranet policies and procedures
## Delegate data collection, extraction, analysis and / or visualization tasks to more junior members of the team in support of the delivery of the ML / AI use cases
## Provide direction for and review the assigned work
## Provide learning and growth opportunities to support the professional development of other team members
## Graduate degree in quantitative field (Data Science, Statistics, Economics, Computer Science, Engineering, or related). Equivalent professional experience will be considered.
## 5+ years of proficiency in Python programming language, with a strong emphasis on Machine Learning applications.
## Proven experience building
advanced regression and predictive models
, including
boosting, model stacking, and non-parametric methods
##
Strong foundation in statistical modeling and inference,
able to design experiments, assess model assumptions, and apply appropriate statistical techniques to real-world data.
## Practical experience with cloud computing environments (Azure, AWS, or Google Cloud) and distributed data processing (PySpark).
## Experience with data query languages such as SQL.
## Skills in data visualization tools, such as Tableau, for effective presentation of analytical findings.
## Background in
applied econometrics
or
advanced statistical analysis
##
Expertise in time series forecasting
, using traditional and neural network based methods for trend and demand prediction
##
Proficiency in deep learning techniques
(e.g., feedforward, convolutional, or transformer models) for complex regression and pattern recognition tasks.## Experience in
spatial data science
or
real estate valuation models
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