Staff Artificial Intelligence Scientist

Fluence

1010

Houston, TX, USA, United States

Posted on: 2024-09-25

Category: energy

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Employment type:

Full time

Experience required:

Intermediate

Salary

Salary not provided

About the company:

Fluence is the leading global energy storage technology and services company, created and backed by Siemens and AES, two industry powerhouses and pioneers in energy storage. Fluence unites the scale, experience, breadth, and financial backing of the two most experienced icons in energy storage.

Our mission is to create a more sustainable future by transforming the way we power our world. Energy storage is critical to this transformation, yet today the market is fragmented and customers face the challenge of finding a trusted technology partner amidst conflicting technical claims, inexperienced vendors and installers, and new market entrants with limited power sector knowledge.

Fluence brings the proven technology solutions and services that overcome the commercial and regulatory barriers that stand in the way of modernizing our energy networks. We are the partner that can deliver at a global scale with the most experienced and knowledgeable team in the world.

We are seeking a highly skilled Staff AI Scientist with expertise in Generative AI, Reinforcement Learning, Deep Learning, optimization, and battery technology to join our Battery Systems Engineering team. The ideal candidate will lead and contribute to the development of innovative machine learning models and algorithms to enhance battery performance, management, and safety. This role will involve working closely with cross-functional teams to support data-driven decision-making and drive the development of advanced battery intelligence solutions.

What does a Staff Artificial Intelligence Scientist do at Fluence?

Algorithm Development: Design, develop, and implement machine learning algorithms, including Generative AI, Reinforcement Learning, Deep Learning, and Optimization models, to optimize battery performance and management.

Data Analysis: Analyze large volumes of battery performance and health data to extract meaningful insights and support the development of predictive models.

Model Training and Validation: Train and validate machine learning models using real-world battery data, ensuring high accuracy and reliability.

Research and Innovation: Stay up to date with the latest advancements in machine learning, AI, Deep Learning, optimization, and battery technology. Apply innovative techniques to solve complex problems in battery systems.

Collaboration: Work closely with data scientists, battery engineers, product managers, and other stakeholders to define project requirements and deliver data-driven solutions.

Performance Monitoring: Monitor and evaluate the performance of deployed models, making necessary adjustments to improve accuracy and efficiency.

Documentation: Document algorithms, models, and processes to ensure transparency and knowledge sharing within the team.

Technical Leadership: Provide guidance and mentorship to junior team members, fostering a collaborative and innovative team environment.

What does the ideal candidate look like?

Education: PhD or Master's degree in Computer Science, Electrical Engineering, Data Science, or a related field.

Experience: Minimum of 3-5 years of experience in machine learning, AI, and data science, with a focus on Generative AI, Reinforcement Learning, Deep Learning, and optimization. Experience in the battery or energy storage industry is highly desirable.

Technical Skills:

Proficiency in machine learning frameworks and tools (e.g., TensorFlow, PyTorch).

Strong programming skills in Python and familiarity with data science libraries (e.g., NumPy, pandas, Scikit-learn).

Experience with innovative AI models such as Generative AI (e.g., GANs, VAEs, LLMs), Reinforcement Learning techniques, Deep Learning, and optimization methods as well as classic statistical inference, estimation, and regression approaches such as Kalman filter, GPR, and Bayesian inference.

Knowledge of battery technology and energy storage systems.

Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and version control tools (e.g., GitHub, GitLab).

Analytical Skills: Excellent analytical and problem-solving skills with the ability to interpret complex data and provide actionable insights.

Communication Skills: Strong verbal and written communication skills that can convey complex technical concepts to non-technical stakeholders.

Attention to Detail: Prominent level of accuracy and attention to detail in data processing and analysis.

Desired Experience and Skills:

Battery Technology: Deep understanding of lithium-ion electrochemistry and battery modeling techniques via self-study, academic research, dual degree programs, trainings, or industrial experience.

Machine Learning Research: Peer-reviewed publications in machine learning, AI, or battery technology, particularly in top conferences such as ICLR, NeurIPS, ICML, and AAAI.

Predictive Modeling: Experience in developing and validating predictive models for battery state estimation, health monitoring, and fault detection.

Agile Methodologies: Familiarity with agile methodologies and project management tools (e.g., Jira).

Visualization Tools: Experience with data visualization tools (e.g., Tableau, Power BI).

Control Systems: Knowledge and experience in control systems.

Power Systems Applications: Experience with grid following and grid forming power systems applications.

Fluence

1010

Full time

Fluence

1010

Full time

Energy