At a Glance
- Tasks: Build and optimise data pipelines while leading AI and Machine Learning initiatives.
- Company: Join a dynamic team at a forward-thinking media company.
- Benefits: Remote work, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on diversity and innovation.
- Why this job: Make an impact by driving AI adoption and enhancing data capabilities.
- Qualifications: Experience in data engineering, SQL, and machine learning required.
The predicted salary is between 50000 - 70000 £ per year.
Reports to: Director of Data Science & Engineering
We are looking for a Data Engineer with Machine Learning experience to join our team. They will be responsible for maintaining and supporting the existing data infrastructure used to underpin our data analytics and reporting. The Data Engineer will own the company's drive into Machine Learning and AI adoption. They will be accountable for building as well as owning new Machine Learning and AI solutions across the team and the wider business. These must complement our current, scalable data architecture.
The Data Engineer will support the customer analytics function by developing and deploying machine learning models and techniques to deliver value around our consumer (B2C) data. They will be expected to optimize the architecture of our data pipelines and ensure that data flows support various cross-functional teams across the business. The ideal candidate will have a self-directed, innovative mindset who is comfortable supporting the data needs of multiple teams. The right candidate will be proactive in identifying and implementing improvements for our systems contributing constructively to the current data ecosystem.
As the business continues to invest in cloud solutions, particularly Google Cloud Platform, you will be excited by the prospect of owning new projects, propelling our data initiatives and capabilities. The Data & Marketing department is the intelligence powerhouse of the business, representing the customer. Our goal is to inspire, engage and grow a loyal readership by harnessing data-driven insights and innovative marketing to deliver personalised, impactful content experiences that build lasting trust, diverse revenue streams, and a thriving future for quality journalism.
An accomplished ML/Data Engineer with proven experience deploying and managing ML and AI based services in production. Strong SQL skills and hands-on experience with both relational and non-relational databases, supporting data needs in fast-paced, content-driven environments. Strong expertise in designing and maintaining scalable data pipelines and architectures—integrating data from web analytics, content management systems (CMS), subscription platforms, ad tech, and social media.
Proven ability to automate and optimise data workflows, using modern ETL/ELT tools (e.g., Airflow, dbt, Apache Spark) to ensure timely and reliable delivery of data. Experience building robust data models and reporting layers to support performance dashboards, user engagement analytics, ad revenue tracking, and A/B testing frameworks. Skilled in cloud-based data platforms and infrastructure (e.g., AWS, GCP), ensuring scalability and security for large volumes of streaming and batch data. Additionally, experience with data warehouses such as BigQuery or Snowflake.
Adept in Python and/or Java for developing data services and integrating APIs to bring in diverse sources of media data. Exposure to utilising AI and Agentic workflows to build and deploy models agents and coding pipelines. Ideally having experience in Gemini, Claude and Vertex AI. Exposure to ML Frameworks such as PyTorch or TensorFlow. Understanding of ML Ops such as automated model testing, monitoring and tuning.
Excellent interpersonal and communication skills, enabling effective collaboration with analytical and commercial teams to turn data into actionable insights. Proactive and self-driven, capable of managing multiple data projects in a high tempo setting while meeting tight deadlines. A continuous learner with a diligent approach to data engineering including data privacy.
Diversity, Equity and Inclusion: We champion diversity in our teams and in our reporting. As a growing and global brand, we must have a workforce that’s more representative of our readers, viewers, clients and partners, and a workplace that creates a sense of belonging for everyone.
- Design and Maintain Data Pipelines: Develop and maintain robust, scalable, and efficient data pipeline architecture to support current and future business needs. Identify, design, and implement improvements to automate manual processes, enhance data delivery performance, and re-architect infrastructure for improved scalability and resilience.
- AI Agentic Workflows: Build on the current usage of AI across the business rolling out new processes and models.
- Machine Learning: Take the lead in building out our capability in this area with new models and recommender systems.
- ETL Development and Infrastructure Building: Build and manage the infrastructure necessary for optimal ETL or ELT of data using Python, SQL, and Google Cloud Platform (GCP) big data technologies, such as BigQuery, Dataflow, Dataproc and Cloud Storage.
- Business Intelligence Enablement: Prepare and transform pipeline data to support downstream analytics and feed BI tools (DOMO), enabling data-driven decision-making across the organization.
- Partner with internal stakeholders: Ranging from Data, Commercial, and Editorial teams to executive leadership—to address data-related technical challenges and support their infrastructure needs.
- Enhance Data System Functionality: Collaborate with the Data Team to continuously improve the functionality, flexibility, and performance of data systems and platforms.
- Data Governance and Compliance: Ensure all data is handled responsibly, securely, and in full compliance with the Data Protection Act, GDPR regulations, and the Company’s Code of Conduct.
- SQL and Database Expertise: Strong working knowledge of SQL with hands-on experience querying and managing relational databases, alongside familiarity with a variety of database technologies (e.g., PostgreSQL, MySQL, BigQuery).
- Big Data Engineering: Proven experience designing, building, and optimizing ‘big data’ pipelines, architectures, and datasets, enabling efficient data processing at scale.
- Skilled in performing root cause analysis: On complex internal and external data sources and business processes to resolve issues and uncover opportunities for operational or strategic improvements.
- Unstructured Data Handling: Strong analytical capability for working with unstructured and semi-structured datasets, transforming raw information into actionable insights.
- Data Workflow Development: Expertise in developing and maintaining data transformation processes, managing data structures, metadata, workload dependencies, and orchestration frameworks.
- Large-scale Data Processing: Excellent project management and organizational skills, with experience supporting and collaborating with cross-functional teams in dynamic and evolving settings.
Holds a graduate degree in Computer Science, STEM related quantitative field, with 2+ years of hands-on experience in a data engineering role. Proficient in SQL databases. Skilled in one or more of the following languages, i.e.: Python, Java, Go.
Inclusive: We champion diversity in our teams and in our reporting. We take risks and are always looking to try new ideas in pursuit of excellence.
Data Science Engineer - Remote employer: The Independent
Join a forward-thinking company that values innovation and diversity, offering a dynamic remote work environment for Data Science Engineers. With a strong focus on employee growth, you will have the opportunity to lead exciting projects in Machine Learning and AI, while collaborating with cross-functional teams to drive impactful data initiatives. Our culture promotes continuous learning and inclusivity, ensuring that every team member feels valued and empowered to contribute to our mission of delivering exceptional data-driven insights.
StudySmarter Expert Advice🤫
We think this is how you could land Data Science Engineer - Remote
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data projects, machine learning models, or any relevant work. This gives you a chance to demonstrate your expertise beyond just a CV.
✨Tip Number 3
Prepare for interviews by brushing up on common data engineering questions and practical scenarios. Practice explaining your thought process when solving problems, as this will highlight your innovative mindset.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace Data Science Engineer - Remote
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Science Engineer role. Highlight your experience with machine learning, data pipelines, and cloud platforms like GCP. We want to see how your skills align with our needs!
Showcase Your Projects:Include specific projects where you've built or optimised data architectures or machine learning models. We love seeing real-world applications of your skills, so don’t hold back on the details!
Craft a Compelling Cover Letter:Your cover letter should tell us why you're excited about this role at StudySmarter. Share your passion for data engineering and how you can contribute to our mission. Let your personality shine through!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining our team!
How to prepare for a job interview at The Independent
✨Know Your Data Inside Out
Before the interview, make sure you’re well-versed in the data technologies mentioned in the job description. Brush up on your SQL skills and be ready to discuss your experience with databases like BigQuery or Snowflake. Being able to talk confidently about your past projects involving data pipelines and machine learning will show that you’re the right fit for the role.
✨Showcase Your Machine Learning Experience
Prepare specific examples of machine learning models you've built or deployed. Be ready to explain the challenges you faced and how you overcame them. This is your chance to demonstrate your hands-on experience with ML frameworks like PyTorch or TensorFlow, and how you’ve applied them in real-world scenarios.
✨Emphasise Collaboration Skills
Since the role involves working with cross-functional teams, highlight your interpersonal and communication skills. Share examples of how you’ve collaborated with different departments to solve data-related challenges. This will show that you can effectively turn data into actionable insights while fostering a team-oriented environment.
✨Be Proactive and Innovative
The ideal candidate is self-directed and innovative. Prepare to discuss how you’ve identified and implemented improvements in previous roles. Think of instances where you’ve automated processes or optimised data workflows, as this aligns perfectly with what the company is looking for in a Data Science Engineer.
