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Weather Analytics Internship Program

engelhartBerlin, GermanyPosted 3h agoonsite
via Arbeitnow
<div class="content-intro"><h2><strong>About Us</strong></h2> <p>Engelhart was founded in 2013 by BTG Pactual Group as a commodities trading company. Our business model is “asset light” and highly diversified – giving us the ability to adapt effectively and nimbly to changing market conditions. We have assembled successful multidisciplinary teams, leveraging advanced fundamental analysis with deep quantitative and weather research capabilities.&nbsp; Our activities are underpinned by strong risk management practices and by powerful technology and operational excellence. We have exceptional teams with diverse global backgrounds and decades of experience, and are driven by a highly collaborative culture, across products and competencies.</p> <p>In 2024, Engelhart acquired Trailstone, a global energy trading and technology company. The acquisition provides us with new expertise, analytics and proprietary technology which is being used to provide risk management and optimisation services to help maximise the value of our clients’ renewable power. The acquisition also expanded Engelhart’s capabilities into physical natural gas across North America, a critical fuel to support the energy transition.</p> <p>Our talented and experienced individuals work together according to its four company values: <strong>Performance, Agility, Collaboration, Entrepreneurship</strong>.</p></div><p style="text-align: center;"><strong>Weather Analytics Internship Program</strong></p> <p style="text-align: center;">Six-Month Internship: 15 February – 12 August 2027<br>Berlin (full-time, on site)</p> <p>Engelhart’s Internship Program offers a unique opportunity to gain hands-on experience in weather analytics within a global commodities business. The intern will work closely with the global Weather Analytics team in Berlin on data-led and technical projects that support the use, interpretation and visualisation of weather data across the business. To be eligible for consideration, <strong>applicants must be available to commence their six-month internship from 15 February 2027</strong>.</p> <p>We are also hiring for Power Analytics, Renewables Analytics and Renewables Origination Analytics internships in Berlin and Grenoble. These have separate application processes due to their different candidate profiles. Further details are available on our Careers Page.</p> <p><strong>What You Can Expect</strong></p> <ul> <li>Hands-on experience within a specialist weather analytics team based in Belfast and beyond, working on technical projects with practical relevance to energy and commodities markets.</li> <li>Exposure to weather data, forecasting products, APIs and visualisation tools used by commercial and analytical teams.</li> <li>Opportunities to work with Python, weather data formats, and tools used in the broader weather ecosystem, including NWP and AI model outputs.</li> <li>Close learning and mentorship from experienced weather, analytics and technology professionals in a global commodities business.</li> </ul> <p><strong>Key Responsibilities</strong></p> <ul> <li>Work closely with the Weather Analytics team on technical, analytical and data visualisation projects.</li> <li>Support the development, testing or improvement of APIs and tools used to access and distribute weather data and products.</li> <li>Analyse and process weather datasets using Python and other relevant tools.</li> <li>Support the visualisation of weather products in AD and other internal platforms.</li> <li>Work with weather data formats and ecosystem tools, potentially including ECMWF ecCodes.</li> <li>Build understanding of numerical weather prediction, AI weather models and the wider weather data ecosystem.</li> <li>Present findings, outputs or technical progress clearly to weather, analytics and commercial stakeholders.</li> </ul> <p><strong>About You</strong></p> <p>We are looking for a motivated, intellectually curious individual who is keen to apply technical and analytical skills to weather-related challenges in energy and commodities markets. You should be comfortable working with data, learning new tools quickly, and collaborating with weather, trading and technology stakeholders in a fast-paced environment.</p> <p>The following experiences and skills are essential for application:</p> <ul> <li>Strong academic background in Computer Science, Atmospheric Sciences, Meteorology, or a closely related technical or quantitative discipline.</li> <li>Strong Python skills, with the ability to analyse, manipulate and interpret complex datasets.</li> <li>Evidence of applying data analysis, coding or quantitative methods to a practical problem, gained through academic projects, internships, research, personal projects or other relevant experience.</li> <li>Demonstrable interest in weather, forecasting, climate, energy markets or commodities, including an ability to discuss relevant developments gained through academic study, projects, research, or personal interest.</li> <li>Strong analytical and problem-solving skills, with a structured and detail-oriented approach.</li> <li>Effective communication skills and a collaborative mindset.</li> <li>Business-level English proficiency.</li> </ul> <p>The following would be advantageous:</p> <ul> <li>Familiarity with weather ecosystem tools, such as ECMWF ecCodes.</li> <li>Knowledge of numerical weather prediction, AI weather models, weather data formats or meteorological datasets.</li> <li>Experience with APIs, data visualisation, dashboards or internal tooling.</li> <li>Exposure to energy markets, power markets, renewables or commodities would be helpful but is not required.</li> </ul><div class="content-conclusion"><p><em>We believe in inclusivity and are therefore dedicated to ensuring all employees – across gender identity, race, ethnicity, sexual orientation, religion, life experience, background and more – feel welcome and included in the company. We promote diversity because we believe it is essential to our ability to think holistically.</em></p> <p>&nbsp;</p></div>

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