Senior Data Scientist

Peak Power Inc.; Toronto; On-site; CAD 120,000 - 150,000; Full time

Benefits offered by this job

Competitive salary

Equity incentive plan

Comprehensive health benefits

Flexible vacation policy

Professional development stipend

Job summary

Peak Power Inc. in Toronto, Ontario is seeking a Senior Data Scientist / ML Engineer to own the full lifecycle of forecasting and optimization models. The successful candidate will be responsible for implementing secure and scalable data pipelines, developing time-series forecasting models, and utilizing machine learning best practices.

Applicants should have a bachelor's degree in a relevant field and over 5 years of experience in data science. The position offers competitive compensation and comprehensive benefits, emphasizing work-life balance.

Qualifications

• 5+ years practical experience in data science or machine learning.
• Experience deploying containerized applications in cloud environments.
• Strong knowledge of relational and time-series databases.

Responsibilities

• Implement and maintain data pipelines supporting data science projects.
• Own the lifecycle of time-series forecasting models.
• Deploy machine learning models using MLOps best practices.

Skills

Python

Data pipeline development

Machine Learning

Time-series forecasting

Data workflow orchestration

Docker

Apache Airflow

Education

Bachelor's degree in software engineering, computer science or related field

Tools

Postgres

AWS Lambda

Terraform

Job description

Peak Power Overview

Peak Power is a North American cleantech company that delivers end-to-end battery energy storage solutions for large commercial, industrial, and manufacturing facilities operating in IESO, ISO‑NE, PJM, and NYISO. By combining industry-leading peak forecasting and market intelligence, we help large energy users significantly reduce electricity costs, unlock new revenue streams, and achieve sustainability goals while removing capital cost barriers with a zero‑capex shared‑savings model.

Role Overview

Reporting to the Director of Software Engineering, the Senior Data Scientist / ML Engineer owns the full lifecycle of forecasting and optimization models—from data pipelines and feature engineering to model training, deployment, monitoring, and production support. The position requires strong analytical, problem‑solving, and cross‑functional collaboration skills to deliver reliable forecasts, insights, and operational decisions that improve power utilization for our stakeholders.

Responsibilities

• Implement and maintain secure, reliable, and scalable data pipelines that support data science projects and real‑time energy‑grid data ingestion.
• 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 advanced time‑series forecasting approaches for energy market, grid, asset, and customer use cases.
• Deploy machine‑learning models to production using MLOps best practices—reproducible training workflows, model versioning, inference pipelines, monitoring, and rollback strategies.
• Build and maintain Apache Airflow DAGs that orchestrate data ingestion, model training, batch inference, validation, and downstream reporting workflows.
• Diagnose and resolve data quality, model performance, and pipeline reliability issues with durable fixes that prevent recurrence.
• Support disaster recovery and operational readiness for critical data pipelines, model workflows, and published 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.

Qualifications

• Bachelor’s degree in software engineering, computer science, electrical engineering, physics, mathematics, or related technical field (or equivalent practical experience).
• 5+ years of practical experience across data science, machine‑learning engineering, data engineering, or similar technical roles; strong Python skills and experience moving models from development to production.
• Experience deploying and maintaining containerized cloud applications (e.g., Docker) and cloud functions (e.g., AWS Lambda).
• Proficiency with relational and time‑series databases such as Postgres, TimescaleDB, ClickHouse, and InfluxDB.
• Experience with data workflow orchestration tools such as Apache Airflow or Luigi.
• Experience with MLOps platforms—Kubeflow, AWS SageMaker, or Google Vertex AI.
• Experience with infrastructure‑as‑code tools such as Terraform or Pulumi.
• Experience with large‑scale data processing frameworks (Apache Spark, Apache Flink) is a nice to have.
• Experience building a data lake using Amazon S3 or comparable services.
• General knowledge of software development, APIs, data stores, networking, security, machine‑learning, and cloud‑computing services.
• Self‑sufficiency in troubleshooting and resourcefulness in uncovering and resolving complex problems.
• Continuous learning mindset and a curiosity to understand, seek, and share insights.
• Excellent communication and collaborative problem‑solving skills, with agility in small, cross‑functional teams.

Benefits & Compensation

• Competitive base salary of $120,000–$150,000, commensurate with experience.
• Equity incentive plan participation.
• Sales commission plan.
• Comprehensive health and wellness benefits, paid time off, and flexible vacation policy.
• Professional development stipend for conferences, training, and continuous learning.
• Regular team events, flexible work arrangements, and robust work‑life balance initiatives.

EEO Statement

At Peak Power we value the unique experiences and perspectives that people bring. We welcome applicants of different backgrounds, experiences, abilities, and perspectives, and we are committed to a collaborative and inclusive environment. Accommodations are available for candidates throughout the selection process.

Hiring Process

We may use artificial intelligence (AI) tools to support certain stages of the hiring process. These tools assist our recruitment team by organizing and reviewing candidate information, but they do not replace human judgment. All hiring decisions are made by people. For more information about data usage, please contact us.