“Join us for a unique ML Resident role tackling time-series problems in the energy domain with ML/DL. You’ll collaborate with a dynamic and fast-paced team of machine learning scientists and domain experts, developing innovative models and products with energy data.”

• Maithrreye Srinivasan, Machine Learning Scientist and Dave Staszak, Lead Machine Learning Scientist

REQUIRED SKILLS / EXPERTISE

We’re looking for a talented and enthusiastic individual with solid knowledge of machine learning and experience working with time series data.

REQUIRED QUALIFICATIONS:

• Completion of a Computing Science or ML graduate program, MSc. or Ph.D
• Research or project experience working with time series data and classical time series models (ARIMA, Facebook Prophet, etc.)
• Solid understanding and experience in applications of deep learning techniques such as sequence models (RNNs, LSTMs, GRUs, Transformers, etc.) or multi task learning
• Proficient in Python programming language and related ML frameworks, libraries and toolkits (e.g. Scikit learn, Keras, Tensorflow, Pandas, Jupyter notebooks)
• A positive attitude towards learning and understanding a new applied domain
• Must be legally eligible to work in Canada

PREFERRED QUALIFICATIONS:

• Publication record in peer-reviewed academic conferences or relevant journals in machine learning
• Familiarity with time series anomaly detection methods
• Experience/familiarity with software engineering best practices
• Experience using cloud platforms (GCP, AWS, Azure, etc.)

NON-TECHNICAL REQUIREMENTS:

• Desire to take ownership of a problem and demonstrated leadership skills
• Interdisciplinary team player enthusiastic about working together to achieve excellence
• Capable of critical and independent thought
• Able to communicate technical concepts clearly and advise on the application of machine intelligence
• Intellectual curiosity and the desire to learn new things, techniques, and technologies

ABOUT THE ROLE

This is a paid residency that will be undertaken over a twelve-month period with the potential to be hired by our client afterwards (note: at the discretion of the client and with the requirement of being located in Calgary at that time). The resident will be reporting to an Amii Machine Learning Scientist and regularly consult with the Client team to share insights and engage in knowledge transfer activities.

KEY RESPONSIBILITIES:

• Clean, preprocess, and curate historical energy usage datasets.
• Conduct exploratory data analysis to identify patterns and anomalies.
• Design, build, train, and evaluate ML/DL models
• Develop data and ML workflows
• Undertake applied research on ML techniques to address the limitations in existing models and develop new approaches
• Collaborate with project team and stakeholders to develop minimum viable products (MVPs) and client-centric solutions

“Join us for a unique ML Resident role tackling time-series problems in the energy domain with ML/DL. You’ll collaborate with a dynamic and fast-paced team of machine learning scientists and domain experts, developing innovative models and products with energy data.”

• Maithrreye Srinivasan, Machine Learning Scientist and Dave Staszak, Lead Machine Learning Scientis

Jotson aims to develop a machine-learning solution for detecting anomalies in energy consumption patterns for household and business properties. This solution will alert consumers when energy consumption or charges deviate from expected patterns. Anomaly detection plays a crucial role in monitoring and managing energy usage by flagging unusual consumption patterns or outliers. Anomalies may indicate:

• Equipment inefficiencies or failures.
• Opportunities to detect energy inefficiencies early and take corrective action.
• Misaligned energy systems or operational issues.
• Opportunities for energy savings or optimization.
• Inaccurate metering/billing that can lead to financial losses