Principal Data Scientist (FTC)

Ocado Group
Welwyn Garden City
3 weeks ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist - Remote

Principal Data Scientist (Remote)

Principal / Lead Data Scientist (Basé à London)

Principal Naval Architect (Weights)

Principal Naval Architect (Weights)

Principal Naval Architect (Weights)

Staff Data Scientist - Fulfilment | Welwyn Garden City | Hybrid (2 days office)

12 Month Maternity Cover


About us:

Leading online retail into the future

Ocado Technology is powering the future of online retail across the globe through disruptive innovation and automation. Join us to create world-class systems at the intersection of robotics and IoT, cloud platforms, big data, machine learning, software development, and beyond.

Were constantly reinventing ourselves, learning fast, evolving our craftsmanship and taking risks as we strive to fulfil our mission to change the way the world shops.

We enable some of the worlds most forward-thinking retailers to do grocery online profitably, scalably and sustainably. Over the past two decades, we have developed a wide technology estate that includes robotics, AI and machine learning, simulation, forecasting and edge intelligence which all form part of our game changing Ocado Smart Platform product.

We champion a value-led culture to get our teams working at their very best and to help create a collaborative working environment that our people love. Core values of Trust, Autonomy, Craftsmanship, Collaboration and Learn Fast help drive our innovative culture.


About the role:

Data Scientists within the Fulfilment department research, design and build models and algorithms to optimise decisions. This covers areas across In-Store Fulfilment (where we continuously look for ways to further optimise manual order picking) and CFC (our highly automated fulfilment centres). We are looking for a Senior Data Scientist to join our Fulfilment Data Science team who are based in Welwyn Garden City. This role will be a 12 month contract to cover one of our team members maternity leave. The ideal candidate will be able to demonstrate proficiency in data science (for predictions as well as optimisation) and be able to collaborate with other crafts such as Software Engineering, Product Management, Data Analytics and Data Engineering. The role and responsibilities of this role include:

  1. Understanding and defining the scope of problems
  2. Designing and prototyping algorithms
  3. Working with software engineers to get these algorithms into production
  4. Validating the performance of these algorithms in the real world and iterate to improve them if necessary
  5. Sharing knowledge and expertise with the wider group
  6. Encourage knowledge sharing initiatives and help build the data science community.

What were looking for:

  1. A strong data science background including solid coding skills with Python
  2. Scikit learn, Pandas, Matplotlib, Numpy, Tensorflow, Keras
  3. Statistics (including statistical tests)
  4. Good knowledge of traditional Machine Learning techniques (Deep Learning and Reinforcement Learning a definite plus)
  5. Familiarity with Operational Research and optimisation techniques
  6. Knowledge of building Data Science solutions end to end, from discovery to production and maintenance
  7. Familiarity with simulation modelling and tools
  8. Good communication skills
  9. Skilled in collaboration both within and between teams/departments
  10. Experienced in Agile e.g. Kanban and Continuous Delivery
  11. Experienced in scoping out and setting technical direction on projects

Bonus points if you also have experience in some or all of the following areas (in no particular order)

  1. Experience in Operational Research and optimisation techniques (exact methods such as Linear Programming or Constraint Programming and heuristics such as Local Search or Genetic Algorithms)
  2. Experience with data analysis and visualisation tools such as Jupyter, pandas, matplotlib.
  3. Experience with big data technologies (e.g. BigQuery, Spark, GCS, AWS, etc.).
  4. Experience using Kubernetes, Docker and Cloud computing platforms (GCP, AWS)

What do I get in return:

  1. Hybrid working (2 days in the office)
  2. 30 days work from anywhere globally
  3. Remote working for the month of August
  4. Wellbeing support through Apps such as Unmind and an Employee Assistance Programme
  5. 25 days annual leave, rising to 27 days after 5 years service (plus optional holiday purchase)
  6. Pension scheme (various options available including employer contribution matching up to 7%)
  7. Private Medical Insurance
  8. 22 weeks paid maternity leave and 6 weeks paid paternity leave (once relevant service requirements complete)
  9. Train Ticket loan (interest-free)
  10. Cycle to Work Scheme
  11. Opportunity to participate in Share save and Buy as You Earn share schemes
  12. 15% discount on Ocado.com and free delivery for all employees
  13. Income Protection(can be up to 50% of salary for 3 years) and Life Assurance(3 x annual salary)

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 Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.