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About the Role

As a senior applied ML practitioner, you will own the full lifecycle of forecasting and optimization models, from data pipelines and feature engineering through model training, deployment, monitoring, and production support. This work matters, as optimizing power utilization has a huge impact on customers, the electrical grid, and the environment. We offer exciting opportunities for conferences, continuous learning, and professional development on a cutting-edge platform with no tech debt.

Responsibilities

Implement and maintain secure, reliable, and scalable data pipelines for data science projects and real-time energy grid data.
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 to orchestrate data ingestion, model training, batch inference, validation, and downstream reporting workflows.
Diagnose and troubleshoot data quality, model performance, and pipeline reliability issues, implementing durable fixes.
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 to make production data and ML systems understandable to stakeholders.
Collaborate with software teams to understand and document data sources from production applications.
Collaborate with stakeholders to integrate models, forecasts, and 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 advances in time-series forecasting, MLOps, cloud data platforms, and energy analytics.
Apply a pragmatic, production-focused mindset to model development, balancing accuracy, interpretability, maintainability, and operational value.

Requirements

Strong analytical and problem-solving skills to find solutions to complex problems and drive high-risk initiatives to completion on time and on budget.
Experience deploying and maintaining containerized cloud applications (e.g., Docker) and cloud functions (e.g., 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.
General knowledge of software development, APIs, data stores, networking, security, machine learning, and cloud computing services.
Self-sufficient in troubleshooting and resourceful in uncovering mysteries.
Continuously learning new frameworks and technologies to generate innovative solutions.
Curious, striving first to understand before being understood.
Strong analytical skills to find solutions to complex problems and drive initiatives.
Excels when collaborating with a small team using an agile process.
Great communication and collaborative problem-solving skills.

Skills

Python
Time-series forecasting
MLOps
Cloud data platforms
Energy analytics
Data pipelines
Feature engineering
Model training
Model deployment
Model monitoring
Production support
Temporal Fusion Transformer models
Airflow
Docker
AWS Lambda
Postgres
TimescaleDB
ClickHouse
InfluxDB
Kubeflow
AWS SageMaker
Google Vertex AI
Terraform
Pulumi
Apache Spark
Apache Flink
Amazon S3

Experience Level

5+ years of practical experience across data science, machine learning engineering, data engineering, or similar technical roles.

Education Level

Bachelor’s degree in software engineering, computer science or related technical field (e.g. EE, physics or mathematics), or equivalent practical experience
AWS certifications, or equivalent practical experience

Salary/Compensations

$120,000 - $150,000

Benefits

Comprehensive benefits from Day 1
Generous and flexible vacation and sick/wellness days
Half days off before long weekends
Monthly reimbursements for fitness and health journey
Paid time off
Sales Commission Plan
Participation in the organization’s equity incentive plan

About the Company

Peak Power is a growth-stage clean technology company focused on solving problems that impact energy markets locally and globally.
We have partnered with major names in real estate, electricity, and smart city spaces.
We are on the cutting edge of the global transition to distributed, clean, and carbon-free energy.

Equal Opportunity

At Peak Power we value the unique experiences and perspectives that folx bring. This is how we build a collaborative and innovative environment. As such, we welcome people of different backgrounds, experiences, abilities, and perspectives.
Should you feel that you don’t meet 100% of the areas of this posting we encourage you to apply and tell us more about what values you feel you could add to the team.
Accommodations are available by request for candidates taking part in all aspects of the selection process.