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- Tasks: Join us as a Data Scientist to modernise forecasting models and develop innovative data products.
- Company: Fastmarkets is a leading price-reporting agency for global commodities with a rich history since 1865.
- Benefits: Enjoy a hybrid work model, inclusive culture, and opportunities for professional growth.
- Why this job: Be at the forefront of AI and machine learning in a dynamic, collaborative environment.
- Qualifications: Proficiency in Python or R, experience with AI techniques, and a passion for data science are essential.
- Other info: We value diversity and encourage applicants from all backgrounds to apply.
The predicted salary is between 36000 - 60000 £ per year.
Company Description
Fastmarkets is an industry-leading price-reporting agency (PRA) and information provider for global commodities, providing price data, news, analytics and events for the agriculture, forest products, metals and mining and new-generation energy markets.
Fastmarkets\’ data is critical for customers seeking to understand and predict dynamic, sometimes opaque markets, enabling trading and risk management. Fastmarkets is a global business with a history dating back to 1865 and is built on trust and deep market knowledge. It has more than 600 employees spread across global locations in the UK, US, China, India, Singapore, Brazil, Belgium, Finland and beyond.
Job Description
The Role
- The Data Scientist will play a pivotal role in partnering with commodity subject matter experts to develop new forecasts and data sets, while also transforming the company\’s existing commodity price forecasting and benchmarking capabilities into scalable, efficient solutions using Python, R, and other advanced analytics platforms. This role is central to enhancing the accuracy, agility, efficiency and transparency of forecasting processes. The Data Scientist will also lead the integration of artificial intelligence and machine learning techniques to uncover patterns, improve predictive performance, increase automation and efficiency, and develop new products and tools. By bridging traditional methods with new technologies, the Data Scientist will help drive innovation and operational excellence in a data-driven environment. Key metrics for success in this position include delivering timely updates to forecasting models and data products, ensuring measurable improvements in forecast accuracy for key commodity benchmarks and successfully deploying AI-powered analytics tools with minimal issues, while maintaining strong stakeholder engagement and communication.
The ideal candidate will thrive in a fast-paced and innovative environment, have a can-do attitude, and can juggle a wide variety of tasks and projects.
Principal accountabilities
- Modernize Forecasting Models – Support rebuilding and enhancing existing Excel-based commodity price forecasting models using Python, R, or other advanced analytics tools to improve scalability, maintainability, and performance.
- Develop New Models and Analysis – Develop new forecasts and related data sets that drive business insights and supports decision-making for Fastmarkets clients.
- Integrate Artificial Intelligence – Design and implement AI and machine learning models, including generative AI and LLMs to enhance forecasting accuracy, automate insight generation, develop intelligent data products, and create conversational analytics interfaces, such as Chatbots and Agents.
- Data Pipeline Development – Develop robust data pipelines to ingest, clean, and transform large datasets from internal and external sources for use in forecasting and cost benchmarking models.
- Model Validation and Performance Monitoring – Establish validation frameworks and performance metrics to ensure model reliability and transparency.
- Stakeholder Collaboration – Partner with commodity subject matter experts to understand their forecasting processes and techniques, translate them into intuitive technical solutions, and communicate insights effectively.
- Support Analytics Product Development – Develop new and innovative data products that complement forecast models to drive business insights and supports decision-making for Fastmarkets clients.
- Documentation and Knowledge Sharing – Maintain clear documentation of models, methodologies, and assumptions, and contribute to a culture of continuous learning and innovation.
Key interfaces
The Data Scientist is a critical member of Fastmarkets Analytics, a global team of 90+ analysts, economists and engineers at Fastmarkets that produce commodity forecasts, cost and emissions benchmarking, and related data.
- Partner with Analysts and Economists to translate commodity market expertise into robust, data-driven forecasting and benchmarking models.
- Collaborate closely with the Technology and Data Operations teams, including full stack and data engineers, to ensure seamless integration of data pipelines, scalable infrastructure for model deployment, and alignment on data governance and quality standards.
- Partner with data scientists across Fastmarkets to align methodologies, share domain-specific insights, and co-develop scalable models that support enterprise-wide analytics initiatives and foster a unified data strategy.
- Partner with the Product team to translate customer needs into data-driven features and functionality, ensuring that new data products align with market requirements and deliver measurable value to B2B clients.
Qualifications
We recruit talented, dynamic people with diverse backgrounds and experiences, all united by a belief in our mission to provide the world\’s leading and most trusted price reporting, events, and intelligence service for the markets we serve. We\’re proud to be an equal opportunities employer and are committed to creating a fully inclusive workplace, where everyone feels able to participate and contribute meaningfully.
If you are open-minded, curious, resilient, solutions-oriented and committed to promoting equality, then read on…
KNOWLEDGE, EXPERIENCE AND SKILLS
We are looking for an individual who is highly motivated, driven, and have a passion to be part of a fast-paced, successful team. Being a strong team player is also important as well as someone who is happy to work flexibly.
- Proficiency in Python, R, or similar languages, familiarity with data science libraries (e.g., pandas, scikit-learn, statsmodels, Prophet)
- Experience applying Artificial Intelligence techniques, including generative AI and large language models (LLMs).
- Experience with SQL and Snowflake for querying and managing large datasets and Snowpark for Python for data processing, engineering, and machine learning
- Familiarity with dbt Cloud or similar tools for transforming data
- Strong foundation in statistics, econometrics, and machine learning, with a focus on time series forecasting and predictive modelling a plus.
If you\’re excited about the role but your experience, skills or qualifications don\’t perfectly align, we encourage you to apply anyway.
Additional Information
Our Values
Fastmarkets people come from all different walks of life. It\’s this mix of brilliant personalities, experiences and insights that gives us that warm, open, and friendly culture you can feel as soon as you meet us. But however wonderfully different we all are, there are six things we all have in common – and they form our Fastmarkets values.
Created by our own employees to reflect some of the personal traits that Fastmarkets people have, our values are key to what makes our culture unique. They reflect who each of us are and they\’re embedded in everything we do. Our values are:
- METRICS DRIVEN. We use insights to improve our customers\’ experience and our business performance
- ACCOUNTABLE. We are accountable to ourselves and those we work with: we keep our promises and get things done
- GROWTH MINDSET. This value enables us to be nimble to the changing realities and operate with a sense of urgency
- INCLUSIVE. We are inclusive and respectful, celebrating each of us and giving everyone a deep sense of belonging with the desire to bring their best self to work every day.
- CUSTOMER CENTRIC. We are customer-centric in all that we do
- COLLABORATIVE. We are collaborative, able to work across teams and capitalise on the diversity of intellect, perspectives, and experiences.
You\’ve read a little about us – now it\’s over to you!
If you like what you\’ve read so far and think you can see yourself as a Fastmarkets person, it\’s time to fill in your application form. This form is an important part of the selection process: it\’s used to determine whether or not you\’ll be chosen to have an interview and acts as a basis for the questions we\’ll ask you on the day.
It\’s vital that you try to capture all the relevant information we have asked for on the form so we can get a good feel for who you are and why you\’re great.
Videos To Watch
https://www.youtube.com/watch?v=kxxaZSMU3OE&t=1s
https://youtu.be/Vmu6NKvGxFM #J-18808-Ljbffr
Data Scientist (Hybrid) employer: Fastmarkets
Contact Detail:
Fastmarkets Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist (Hybrid)
✨Tip Number 1
Familiarise yourself with the specific tools and technologies mentioned in the job description, such as Python, R, and SQL. Being able to discuss your experience with these languages and how you've used them in past projects will show that you're a strong fit for the role.
✨Tip Number 2
Research Fastmarkets and their approach to data science and analytics. Understanding their values and how they apply to their work can help you align your answers during interviews and demonstrate your enthusiasm for joining their team.
✨Tip Number 3
Prepare examples of how you've successfully collaborated with cross-functional teams in previous roles. Highlighting your ability to work with analysts, economists, and technology teams will showcase your teamwork skills, which are crucial for this position.
✨Tip Number 4
Stay updated on the latest trends in AI and machine learning, especially in relation to forecasting and data analysis. Being able to discuss recent advancements or case studies will demonstrate your passion for the field and your commitment to continuous learning.
We think you need these skills to ace Data Scientist (Hybrid)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience and skills that align with the Data Scientist role. Emphasise your proficiency in Python, R, and any AI techniques you've applied, as well as your experience with data science libraries.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for data science and how your background fits with Fastmarkets' mission. Mention specific projects or experiences that demonstrate your ability to modernise forecasting models and integrate AI.
Showcase Your Problem-Solving Skills: In your application, provide examples of how you've tackled complex data challenges in the past. Highlight your analytical thinking and how you’ve used data to drive business insights, which is crucial for this role.
Highlight Collaboration Experience: Since the role involves working closely with commodity experts and other teams, mention any previous collaborative projects. Discuss how you effectively communicated insights and contributed to team success.
How to prepare for a job interview at Fastmarkets
✨Showcase Your Technical Skills
Be prepared to discuss your proficiency in Python, R, and any relevant data science libraries. Bring examples of past projects where you've applied these skills, especially in forecasting or machine learning.
✨Understand the Company and Its Values
Familiarise yourself with Fastmarkets' mission and values. Be ready to explain how your personal values align with theirs, particularly around being metrics-driven, accountable, and customer-centric.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities and how you would handle real-world data challenges. Think about how you would modernise forecasting models or integrate AI techniques in practical scenarios.
✨Demonstrate Collaboration Skills
Since the role involves working closely with commodity experts and other teams, be ready to share examples of how you've successfully collaborated in the past. Highlight your ability to communicate complex ideas clearly and effectively.