At a Glance
- Tasks: Lead data analysis and model development for intelligent personalisation systems.
- Company: Beyond, a tech consultancy driving innovation and inclusivity.
- Benefits: Competitive salary, diverse culture, and opportunities for professional growth.
- Why this job: Join a team shaping the future of AI and personalisation in a dynamic environment.
- Qualifications: Advanced degree in a quantitative field and 5+ years of data science experience.
- Other info: Embrace a culture of creativity and inclusivity while tackling real-world challenges.
The predicted salary is between 48000 - 84000 £ per year.
Beyond is a technology consultancy helping organizations thrive in a rapidly changing world. We build, modernize, scale, and operationalize technology, creating Cloud and AI solutions to unlock productivity and drive customer growth.
Role Overview
We're looking for a deeply investigative and creative Senior Data Scientist to uncover opportunities and define the models that power our next generation of intelligent personalization systems. This role is for a true practitioner who is comfortable with ambiguity and excels at translating complex business problems into rigorous, data-driven solutions. You will be the team's expert in algorithmic design and statistical modeling, with a strong focus on prototyping novel approaches for Next Best Action (NBA) and sophisticated feed/collection ranking. You will explore, analyze, and model complex datasets within the GCP ecosystem to validate hypotheses and create the blueprints for production-scale models.
What You'll Do
- Lead the exploratory analysis and data investigation to identify new features and modeling opportunities for personalization, ranking, and contextual nudges.
- Design, prototype, and validate a wide range of machine learning models, with a deep focus on algorithms for Next Best Action (NBA) and Learning to Rank (LTR).
- Apply your expertise in statistical analysis, causal inference, and experimental design to build and interpret robust A/B testing frameworks, ensuring model improvements are statistically significant.
- Formulate and test hypotheses in an ambiguous problem space, demonstrating a "fail-fast" and iterative approach to model development.
- Collaborate closely with ML Engineers to define model requirements, feature needs from feature stores, and scalable serving logic.
- Research and adapt cutting-edge academic techniques in machine learning, recommendation systems, and optimization to solve real-world business challenges.
- Prototype models for marketing technology use cases, leveraging customer attributes from diverse sources (like Oracle) to prove potential uplift.
- Communicate complex findings and model mechanics clearly to both technical and non-technical stakeholders.
What We're Looking For
- Advanced degree (MSc or PhD) in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
- 5+ years of hands-on experience as a Data Scientist, applying advanced statistical and machine learning techniques to complex business problems.
- A deeply investigative and curious mindset, with a proven ability to navigate ambiguous problems and deliver concrete, data-driven insights.
- Expert-level theoretical and practical knowledge of ML algorithms, including a strong understanding of the mathematics behind models used for ranking (e.g., LTR), recommendations (e.g., collaborative/content filtering, matrix factorization), and decisioning (e.g., uplift modeling, reinforcement learning concepts).
- Mastery of Python and its core data science/ML ecosystem (e.g., Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, Jupyter).
- Strong experience with SQL and data manipulation, ideally within a cloud environment like GCP BigQuery.
- Experience in designing and analyzing large-scale A/B experiments.
- A proactive, self-starting mentality and exceptional problem-solving skills.
Nice to Have
- Familiarity with the GCP cloud stack, particularly Vertex AI.
- Experience with Datadog or other monitoring tools for analyzing model performance.
- Published research in relevant ML, IR (Information Retrieval), or RecSys (Recommender Systems) conferences.
- Experience applying LLMs for personalization or search tasks.
- Experience with reinforcement learning (RL), especially for NBA or dynamic systems.
- Familiarity with Oracle databases as a data source.
Having been named among the Sunday Times Best 100 Companies, we believe culture plays a large role in what we offer as an organization. We actively promote diversity in all its forms across our Studios, and we proudly, passionately, and proactively strive to create a culture of inclusivity and openness for all our employees. Beyond is committed to welcoming everyone, regardless of gender identity, orientation, or expression. Our mission is to remove exclusivity and barriers and encourage new thinking and perceptions in a space of belonging. It is not about race, gender, or age, it is about people. And without our people being their most creative and innovative selves, we are nothing.
Senior Data Scientist - Personalisation employer: Beyond
Contact Detail:
Beyond Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist - Personalisation
✨Tip Number 1
Network like a pro! Reach out to current employees at Beyond on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for landing the Senior Data Scientist role. Personal connections can make a huge difference!
✨Tip Number 2
Prepare for those tricky interview questions! Brush up on your algorithmic design and statistical modelling knowledge. Be ready to discuss your past projects and how you tackled complex data problems. Show us your investigative mindset!
✨Tip Number 3
Don’t forget to showcase your creativity! When discussing your experience, highlight any innovative approaches you've taken in your previous roles. We love seeing how candidates think outside the box, especially when it comes to personalisation and machine learning.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows your genuine interest in joining Beyond. Let’s get you on board!
We think you need these skills to ace Senior Data Scientist - Personalisation
Some tips for your application 🫡
Show Your Passion: When writing your application, let your enthusiasm for data science and personalisation shine through. We want to see how your curiosity drives you to explore complex datasets and uncover insights that can make a real difference.
Tailor Your Experience: Make sure to highlight your relevant experience in statistical analysis and machine learning. We’re looking for someone who can navigate ambiguity and deliver data-driven solutions, so share specific examples of how you've done this in the past.
Be Clear and Concise: While we love detail, clarity is key! Use straightforward language to explain your skills and experiences. Remember, you’ll be communicating with both technical and non-technical stakeholders, so make it easy for everyone to understand your expertise.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates about the role. Plus, we love seeing applications come in through our own channels!
How to prepare for a job interview at Beyond
✨Know Your Algorithms
Brush up on your knowledge of machine learning algorithms, especially those related to ranking and recommendations. Be ready to discuss how you’ve applied these in past projects, and think about how you can adapt them for the role at Beyond.
✨Prepare for Ambiguity
Since this role involves navigating ambiguous problems, come prepared with examples of how you've tackled uncertainty in previous projects. Share your thought process and how you iterated on solutions to find success.
✨Showcase Your Statistical Skills
Be ready to dive deep into statistical analysis and A/B testing frameworks. Prepare to discuss specific experiments you've designed, the outcomes, and how you ensured the results were statistically significant.
✨Communicate Clearly
Practice explaining complex data science concepts in simple terms. You’ll need to communicate findings to both technical and non-technical stakeholders, so think about how you can make your insights accessible and engaging.