Peak Power is a North American cleantech company providing end-to-end battery energy storage solutions for large commercial, industrial, and manufacturing facilities operating in IESO, ISO-NE, PJM, and NYISO. Powered by industry-leading peak forecasting and market intelligence, we help large energy users significantly reduce electricity costs, unlock new revenue streams, and achieve sustainability goals. Through strategic investor partnerships, we remove capital cost barriers with zero-capex energy storage offered through a shared‑savings model, aligning with customer success by prioritizing battery performance.

Reporting to the Director of Software Engineering, you will work closely with Software Engineers, Product, Operations, and business stakeholders to own the full lifecycle of forecasting and optimization models: from data pipelines and feature engineering through model training, deployment, monitoring, and production support. As a senior applied ML practitioner, you will be responsible for both the data systems and the machine learning workflows that produce reliable forecasts, insights, and operational decisions for our stakeholders. Even a small change to optimize power utilization can have a huge impact on our customers, our electrical grid and our environment.

We’re building something that will have a lasting, positive impact. You have robust analytical and problem‑solving skills to find solutions to complex problems and drive high‑risk initiatives to completion on time, on budget. Implement and maintain data pipelines that are secure, reliable and scalable by design to support data science projects and real‑time energy grid data pipelines Own the full lifecycle of time‑series forecasting models, including data preparation, feature engineering, model training, validation, deployment, monitoring, retraining, and production support.

Develop, train, tune, and operate Temporal Fusion Transformer models and other time‑series forecasting approaches for energy market, grid, asset, and customer use cases. Deploy machine learning models into production using practical MLOps patterns, including reproducible training workflows, model versioning, inference pipelines, monitoring, and rollback strategies.



Build and maintain Airflow DAGs that orchestrate data ingestion, model training, batch inference, validation, and downstream reporting workflows.

Diagnose data quality, model performance, and pipeline reliability issues; Support disaster recovery and operational readiness for critical data pipelines, model workflows, and production forecasting systems. Monitor and report on data pipeline health, data quality, model performance, forecast accuracy, drift, and production incidents. Establish transparent tracking for model experiments, training runs, deployment status, and operational performance.

Create dashboards, alerts, and documentation that make production data and ML systems understandable to engineering, product, and business stakeholders. Collaborate with software teams to understand and document the sources of data from production applications. Collaborate with stakeholders to integrate models, forecasts, and various types of data into production applications.

Provide technical guidance and coaching to influence the design, development and testing of cloud applications that produce data, Continuously improve the reliability, scalability, observability, and performance of data pipelines and production ML systems. Stay current with practical advances in time‑series forecasting, MLOps, cloud data platforms, and energy analytics. Bachelor’s degree in software engineering, computer science or related technical field (e.g.

AWS certifications, or equivalent practical experience 5+ years of practical experience across data science, machine learning engineering, data engineering, or similar technical roles, with strong Python skills and experience taking models from development into production. AWS Lambda) Experience working with relational and time series databases, like Postgres, TimescaleDB,



ClickHouse and InfluxDB Experience with data workflow orchestration tools such as Apache Airflow or Luigi Experience with MLOps platforms such as Kubeflow, AWS SageMaker, or Google Vertex AI Experience with infrastructure‑as‑code software such as Terraform or Pulumi Experience with large‑scale data processing and distributed computing frameworks such as Apache Spark and Apache Flink is a nice to have.

Experience building a data lake using Amazon S3, or similar technologies General knowledge of software development, APIs, data stores, networking, security, machine learning and cloud computing services Excels when collaborating with a small team using an agile process We are a growth‑stage clean technology company that has partnered with major names in real estate, electricity, and smart city spaces. Justice, Equity, Diversity, and Inclusion (JEDI). We believe in driving a better energy future through focused efforts towards understanding and improving Diversity, Equity, and Inclusion.

Generous and adaptable vacation and sick/wellness days to make time for personal appointments and taking a mental health break. We take internal engagement surveys seriously and make actionable changes based on feedback. Get Social.

Fun activities including team events and game nights to stay connected both virtually and in person including fun Slack channels that celebrate our love of food, fitness, fashion, furry friends, and everything in between!

Long Weekends Start

Early. Beat the traffic to the cottage or curl up with your favourite book, start early with half days off before long weekends. Apply here or learn more about our company from our website or LinkedIn.

Accommodations are available by request for candidates taking part in all aspects of the selection process. Our total compensation package includes a competitive salary, comprehensive health and wellness benefits, paid time off, Sales Commission Plan, participation in the organization’s equity incentive plan, along with additional perks designed to support your well‑being and skilled growth. We may use artificial intelligence (AI) tools to support certain stages of the hiring process.

These tools help our recruitment team by organizing and reviewing candidate information, but they do not replace human judgement.

📌 Senior Data Scientist - E-Commerce (Toronto)
🏢 Peak Power
📍 Toronto