Energy Storage Machine Learning & Data Science Leader (Sr. Manager - Director level) - REMOTE

ThinkBAC Consulting; United States; Remote; USD 100,000 - 125,000; Full time

Job summary

Join a pioneering renewable energy firm as a leader in Energy Storage Machine Learning & Data Science. This innovative role focuses on developing advanced ML/AI models that enhance decision-making for utility-scale energy projects across the U.S. You'll lead a creative team in optimizing energy trading strategies and asset management while working with cutting-edge technology. The company values transparency and offers a competitive salary, flexible hours, and a supportive environment to drive impactful projects. If you're passionate about renewable energy and technology, this opportunity is your chance to make a significant difference in the industry.

Benefits

Open PTO policy

Flexible work hours

Comprehensive benefits

Transparent Executive Leadership Team

Qualifications

• 8-10+ years in machine learning software engineering with team leadership experience.
• Expert in MLOps and machine learning model deployment in production environments.

Responsibilities

• Develop AWS Sagemaker based machine learning models for energy trading.
• Create and automate time series forecasting models using deep learning techniques.

Skills

Machine Learning

Python Programming

Deep Learning

Reinforcement Learning

Data Feature Engineering

Agile Methodology

Optimization Techniques

Cybersecurity Best Practices

Education

Bachelor's Degree in Computer Science or related field

Master's Degree in Data Science or related field

Tools

AWS Sagemaker

Pyomo

CVXPY

GurobiPy

PostgreSQL

TimescaleDB

InfluxDB

Job description

This is a remote position.

Energy Storage Machine Learning & Data Science Leader (Sr. Manager to Director level) - Energy Trading
Location: FULLY REMOTE (Anywhere in the USA)

This is an opportunity to join an industry-leading renewable energy venture with strong private equity backing that is focused on the development, execution, and operations of dynamic utility-scale energy storage projects. They are at the forefront of the industry, have accumulated over 9GW of projects in a relatively short period of time, and are currently in an accelerated expansion phase which includes key additions to their Software, Data, and Technology Team.

The Energy Storage Machine Learning & Data Science Leader is an innovative team leadership role focused on ML/AI energy predictive pricing and quantitative forecasting models that will drive the algorithmic decision-making process of next-generation utility-scale renewable energy projects across all ISO/RTO markets in the United States. It will be part of a creative team focused on energy storage/battery storage energy trading strategies, asset management, and real-time energy pricing.

They are committed to creating more renewable infrastructure solutions for the grid and are offering comprehensive compensation packages to their employees leading the drive to meet company goals. Other perks include a competitive base salary, open PTO policy, flexible work hours, benefits, and the opportunity to work with a transparent Executive Leadership Team.

RESPONSIBILITIES:

• Develop and implement AWS Sagemaker based machine learning optimization and predictive models for energy trading wholesale electricity markets with a key focus on energy storage initiatives.
• Create and automate machine learning single-task and multi-task time series forecasting models utilizing deep neural networks (DNN), deep learning (DL), and reinforcement learning (RL) techniques for energy storage, asset management, and energy trading initiatives.
• Develop and implement a framework for testing machine learning models and taking them into production.
• Take ownership of machine learning operations programs to monitor performance and streamline processes including real-time troubleshooting.
• Play a key role in the data feature engineering through work with the team’s Data Engineers to enrich and optimize the data pipeline.
• Utilize forward-thinking techniques such as optimal control, deep learning, machine learning (AI/ML), and reinforcement learning to evaluate and update current protocols.

QUALIFICATIONS:

• 8-10+ years of machine learning software engineering, programming, or coding experience; at least 2 years in Team Leadership roles with direct reports.
• Machine learning development experience in production-ready coding environments focused on complex projects.
• Expert in machine learning model deployment and machine learning operations (MLOps); well versed in agile-based software development methodology.
• Well versed in Python-based optimization toolkits such as Pyomo, CVXPY, GurobiPy, etc.
• Well versed in machine learning concepts such as classification, deep learning, deep neural networks (DNN), reinforcement learning, and regression problem-solving techniques.
• Expertise in the Amazon Web Services (AWS) Sagemaker Machine Learning platform.
• Solid understanding of convex optimization techniques (Linear/Mixed Integer programming) and time-series forecasting (PostgreSQL, TimescaleDB, InfluxDB).
• Solid exposure to cybersecurity best practices for software development and distributed architecture systems.
• HUGE PLUS - experience working in production-ready coding environments in the energy trading or financial trading sector.
• HUGE PLUS - solid understanding of national energy markets and renewable energy portfolios - PJM, ERCOT, SPP, MISO, NYISO, ISO-NE, and CAISO; capacity prices, regional energy pricing, congestion and curtailment analysis, transmission constraints, interconnection assessments, LMPs (locational marginal pricing), and/or regional supply and demand curves.
• Ideal candidates for this role will have experience working in Senior, Lead, Principal, Manager, and Director level roles as Software Architect, Data Scientist, Full-Stack Developer, Machine Learning Engineer, Artificial Intelligence Engineer, ML/AI Engineering Leader, AI Software Engineering Leader, ML Software Development Leader, ML/AI Engineering Manager, etc.