Flower

高级数据科学家

加入Flower斯德哥尔摩团队,担任高级数据科学家,开发AI驱动的短期能源预测模型。负责模型全生命周期管理,协作多领域专家,享受混合办公模式和多元文化环境。
Flower
Flower
斯德哥尔摩,瑞典 混合 全职 UTC+01:00

Flower

公司概况

名称

Flower Infrastructure Technologies AB,私营企业

总部

瑞典,斯德哥尔摩

成立时间

2020年

规模

约200名员工,拥有超过1.5亿资金支持(来源:nasdaq.com

业务介绍

Flower是一家专注于能源技术的公司,专门开发软件和交易系统,以优化灵活的电力资产,而非传统的可再生能源项目开发。其基于人工智能和机器学习的平台管理并交易电池、风电和太阳能电站、电动汽车充电桩及其他分布式能源资源,旨在最大化资产价值和电网稳定性(来源:flower.se)。该平台通过模拟行为、天气和市场数据,实现对充电、放电或灵活资源交易的自动决策,为资产和电网所有者带来可预测性(来源:flower.se)。其产品涵盖电网级电池储能系统、绿色基荷、风能和太阳能发电、需求响应及分布式能源资源,使Flower成为新兴的电网级BESS开发商(来源:flower.se)。公司主要面向灵活能源资产的所有者和运营商,如电池项目、风电和太阳能农场、电动汽车充电基础设施,以及需要调节和平衡服务的电网运营商(来源:business-sweden.com)。

项目与业绩

Flower在瑞典拥有丰富的业绩记录,完成了多项电池收购和部署。2024年9月,公司收购了Arise准备建设的40兆瓦/80兆瓦时电池储能系统项目,该项目是瑞典最大的电池项目之一(来源:tech.eu)。此外,Flower还收购了瑞典最大的电池园区——42.5兆瓦的Bredh lla项目,并于2024年7月激活了Bredh lla、Kung lv和Hanhals三个电池项目,形成了该国最大的电池组合(来源:tech.eu)。公司被公认为电网级电池的领先项目开发商,受到欧洲输配电网电网所有者的信赖(来源:flower.se)。Flower还管理着风电购电协议(PPA)组合,控制着瑞典11个风电场,总计180吉瓦时,彰显其在灵活资产管理和电网平衡中的重要作用(来源:business-sweden.com)。

最新动态

近年来,Flower实现了快速扩张。截至2026年6月,公司拥有超过140名员工和超过1.5亿资金支持,管理着北欧最大的灵活资产组合(来源:nasdaq.com)。2026年8月,Flower的投资者页面展示了其全套1小时电池系统的月度交易表现,并显示通过高级担保债券发行持续扩展,以资助欧洲能源基础设施的发展(来源:flower.se)。2026年7月的一份新闻稿宣布,Flower获得Norion银行的新一轮债务融资,支持其在瑞典的持续增长,收入从2022年的约1000万瑞典克朗增长到2023年的约1亿瑞典克朗(来源:tt.se)。早在2024年9月,Flower完成了4500万欧元的A轮融资,其中包括由Northzone领投的额外2000万欧元,投资者还包括Giant Ventures、82an Invest、Sony Innovation Fund、Thomas von Koch和Sebastian Knutsson(来源:eu-startups.com)。

工作环境

Flower在商业、技术和资产开发等多个职能领域提供职位,体现了其多元化的领导架构,包括工程、产品、运营、资产开发、分布式资产、商业、财务及人力/文化部门(来源:flower.se)。作为一家处于成长阶段的企业,公司拥有约200名员工,斯德哥尔摩总部营造了一个国际化环境,鼓励跨职能协作(来源:linkedin.com)。求职者可期待在软件开发、能源交易、项目开发、运营及企业职能等领域获得机会,加入这一快速发展的能源技术公司(来源:linkedin.com)。


最后更新于 9月 6, 2026 | 报告问题

Job Description

Flower is Flexible Power. We are a next-gen energy company leveraging AI and machine learning to make renewable energy stable and always available - even when the sun isn't shining and the wind isn't blowing.

Through smart optimization and trading of energy assets like wind and solar farms, battery systems, and EV chargers, we make renewable energy reliable and predictable, leading the charge towards the energy system of tomorrow.

Who We Are

Tech company at heart. Purpose-driven at core. Flower consists of a diverse group of innovative individuals with a strong desire to improve the state of the world.

At Flower, we believe trust, collaboration and diversity are essential to not only create an inclusive work environment, but also drive career growth. By embracing varying perspectives, we allow creativity and progress to flourish.

To accelerate towards our goal of becoming the pioneering force powering the energy system of tomorrow, we are now looking for a passionate and skilled Senior Data Scientist to join us!

About The Role

As a Senior Data Scientist, you will shape the foundation of Flower's short-term forecasting capabilities - powering the models that directly drive our daily trading decisions. You will be part of a small, highly skilled team operating at the intersection of data science, energy systems, and close-to-real-time market dynamics.

You will own the full lifecycle of your models - from idea to production - working closely with Data Engineers, Machine Learning Engineers, and Energy Market Experts to ensure our forecasting stack runs smoothly and is continuously improving. This is a hands-on role focussed on time-series modelling, rapid experimentation, and the ability to translate data into high-impact operational decisions.

What You'll Do

  • Build & maintain short term operational forecasting models for energy volumes & prices that drive our trading decisions on a day-to-day basis
  • Build time series models to understand and explain recurring patterns across different measurements, products and regions to distill signals as features for your next model
  • Prototype quickly: iterate on simple models, validate rigorously, and scale what works in a dynamic market environment
  • Own the full model lifecycle: EDA, feature engineering, model development, validation, deployment and continuous evaluation, supported by our team of DEs, MLEs & Energy Market Experts
  • Write your models in Python, version your code in Git, use CI/CD to ensure full reproducibility
  • Partner with DEs to evaluate and integrate new data sources (weather, grid conditions, market signals)
  • Collaborate & mentor across other data scientists, quants and engineers

Who You Are

  • Master's or PhD in Statistics, Machine Learning, Computer Science, Industrial Engineering, Applied Mathematics, Engineering Physics, or a related quantitative field
  • 5+ years building and deploying time-series prediction models in production using machine learning or classical methods
  • Strong command of Python and no stranger to the command line; comfortable writing clean, maintainable, production-ready code
  • Deep understanding of time-series forecasting and real-world modeling challenges
  • Prior experience in energy markets or power systems
  • Strong mentorship and coaching skills
  • Highly driven by our mission and your impact, yet humble enough to know we are all always learning
  • Eagerness to apply yourself in a mostly automated trading floor environment
  • Thrive in a fast-moving environment and adapt models as markets evolve

Location

Our beautiful office is located in the heart of Södermalm, just a short walk from Slussen subway station. We encourage in-office collaboration but support a hybrid work model.

Our corporate language is English, as we have over 30 nationalities in the office. We therefore appreciate it if you could submit your CV in English.

Throughout the recruitment process you will meet with the Talent Partner, Head of Predictions and Optimization, the Team, VP of Engineering and VP of Product.

We look forward to hearing from you!

We kindly but firmly decline any engagement in recruitment assistance for our hiring processes. This includes partnership offers or the sale of recruitment tools.

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职位详情

2026年9月9日

2026年9月9日

全职

混合

公司

太阳能, 风能, 储能, 电动汽车充电基础设施, 智能电网

Flower

flower.se

  •  斯德哥尔摩,瑞典

5+ years

UTC+01:00