Graduate Data Scientist

Brady Technologies Limited
London
2 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist

Data Scientist/Statistician

Principal Data Scientist (Remote)

Principal Data Scientist

Principal Data Scientist - Remote

Principal Data Scientist (Remote)

Graduate Data Scientist (4 months FTC contract with a potential for a permanent position)

London (hybrid)

We have an exciting opportunity for a Graduate Data Scientist to join one our newest teams at Brady working on an exciting cloud-native SaaS solution for the energy and power trading markets. As renewables continue to increase and disrupt the energy mix, Brady are developing a new flagship software product that enables short-term power trading throughout the UK, EU and further afield.

At Brady Technologies, we are at the forefront of the energy transition, helping market participants navigate new challenges and opportunities with confidence. Our advanced software solutions bring clarity to complex problems and processes, enabling customers to enhance their trading and operations and meet tomorrow's energy needs. Our product suite serves a diverse client base, including utilities, independent power producers, renewable asset developers, and energy and multi-commodity trading houses. We also provide solutions for oil & gas companies, particularly as they develop their power businesses, state power grid operators, hedge funds and investment management companies.

Our software solutions support critical decision-making and help these regional and global leaders optimise their trading, power operations, and manage complex risks. Our technology facilitates increased automation and efficiency in the face of changing market dynamics including decentralisation, decarbonisation, diversity of generation assets, volatility and evolving regulation. We are proud of the spirit of partnership we have with our customers, many of whom have been with us for a decade or more. Furthermore, we are committed to our values of collaboration, innovation and delivery, to ensure we continue to meet their and the energy market's needs in the future.

This is an exciting opportunity for a recent graduate with strong analytical and problem-solving skills to gain hands-on experience in data science, machine learning, and algorithmic trading. The successful candidate will work closely with the Lead Data Scientist, contributing to the development of trading algorithms, machine learning models, and interactive data visualizations. This role offers the potential to transition into a full-time permanent Data Scientist position based on performance.

Key Accountabilities:

  • Assist in the exploration and development of energy trading algorithms for profitable trading strategies.
  • Implement and refine machine learning models to enhance market predictions and decision-making.
  • Work with large datasets, applying data transformation and feature engineering techniques.
  • Build and maintain interactive data visualizations using tools such as Shiny to present insights effectively.
  • Collaborate with the Lead Data Scientist and agile development team to integrate models into the trading platform.
  • Participate in agile sprints, contributing to backlog grooming, sprint planning, and retrospectives.
  • Conduct research and stay updated on industry trends, emerging technologies, and best practices in data science and trading strategies.
  • Write clean, efficient, and well-documented Python code for data analysis and modeling.

Key Skills/Experience:

  • Master's degree in Data Science, Computer Science, Mathematics, Statistics, Engineering, or a related field.
  • Strong programming skills in Python and familiarity with data science libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow/PyTorch.
  • Advanced understanding of machine learning concepts and statistical analysis.
  • Strong problem-solving skills and the ability to work with complex datasets.
  • Experience with data visualization tools (e.g, Plotly, Matplotlib, Seaborn). Shiny experience is an advantage.
  • Familiarity with cloud computing platforms (e.g., Azure, AWS) is an advantage.
  • Passion for algorithmic trading, financial markets, or the energy sector is desirable.
  • Excellent communication skills and ability to work in a collaborative team environment.
  • Eagerness to learn and grow in a fast-paced, high intensity, innovative environment.

What Brady offers:

  • Great compensation + 8% pension + 5% bonus + private health insurance and more!
  • 23 days' holiday + bank holiday, increasing by one day per year of service up to 28 days + bank holidays
  • 1/2 day off Christmas Eve & New Year's Eve
  • Pluralsight licenses for engineering team members
  • Flexible working hours
  • An opportunity to build a modern technology platform for the power and energy trading markets
  • A positive, values-driven culture

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.