Quantitative Researcher, Deep Learning

**Grenoble Office | On-site Working | Full-time Role**

**About the Role**

Trailstone seeks to advance the scientific frontiers of forecasting power production for wind and solar parks. As a Quantitative Researcher working at the intersection of deep learning and meteorology, you will play a central role in our mission of making sustainable energy sustainable. This role will empower you to exercise scientific judgement, deep focus and creativity surrounding methods, technologies and data sources needed to deliver cutting-edge forecasting capabilities. Your work will be part of, and supported by, a highly collaborative, international and cross-functional team of Quants, R&D Meteorologists, Developers and Renewable Energy Portfolio Managers.

This will be a full-time role with the flexibility to work from home 1-2 days per week, and will own the following responsibilities:

- Implement and evaluate models from machine learning and deep learning literature, appropriate to forecasting wind and solar power production
- Propose and implement your own models to improve upon standard approaches
- Create an appropriate evaluation framework, including selection of data sets and identification of metrics, to quantify model performances

**About You**

We are looking for someone with strong experience in deep learning and an interest in the renewable energy sector. In addition, we are looking for someone with the following attributes:
**Essential Qualifications**:

- Significant experience with, and a deep understanding of, CNNs, GANs and / or U-Nets
- Intermediate to advanced professional communication skills in English
- Proficiency with Python major deep learning libraries (PyTorch, Keras,) and data manipulation abilities
- Strong academic performance in computer science, applied mathematics, or related disciplines
- Highly motivated, detail-oriented, and driven to deliver high-quality innovative models

**Set Yourself Apart with the following**:

- Academic or industry experience with meteorology and weather data
- Experience with software engineering best practices, e.g. version control, code review, unit/integration testing, and continuous integration and deployment
- Experience with additional scientific and mathematical programming languages
- Demonstrable interest in developing a career in renewable energy and/or energy trading
- Strong interpersonal skills and team-orientation

**About Trailstone**

Trailstone is a global energy and technology company, operating at the intersection of renewable and conventional power. We trade energy in global markets, and we provide a best-in-class service to manage the inherent intermittency of renewable power generation across the short, medium and long term.

As an energy trader, we buy and sell electricity and gas which is physically delivered on the grids, and we leverage our physical market knowledge to trade financial commodities.

As a provider of renewable energy management services, we take on the complexity of managing renewable power across different countries so that our customers can focus on their core mission of investment and growth. Trailstone offers an efficient, fully automated front-to-back renewable power management platform underpinned by our long-standing market experience, proprietary technology, data analytics and meteorology.

**We're different, and we like it that way**

Here at Trailstone, we value our differences. In fact, our success depends upon them.

Differences in backgrounds, identities and experiences lead to differing views of the world. Different world views lead to healthy differences in opinions. Different opinions are essential to creating a robust marketplace of ideas. And it is our ideas that will make Trailstone a market leader in energy innovation and disruption

By our commitment to a diverse and inclusive workforce, Trailstone is creating value for our employees, our investors, and our communities.

So regardless of your gender, race, ethnicity, orientation, physical limitations, how you identify, how you pray, who you love, where you went to school, who you consider family, or how old you areyou can belong here