dunnhumby uses advanced analytics to improve customer understanding and help drive our clients’ growth. We have a large group of applied data scientists working directly with our clients to use data and science to improve the customer experience. This role focuses on the UK business to begin with, working directly with Tesco or in partnerships with their FMCG partners.
With 100s of millions of weekly data points, collected from our clients, the ways that we can generate value by creatively looking at data are vast. The Data Scientist role is an integral member of this team and you will be involved in hands-on research and development of data-driven solutions to real world problems. The work will focus on implementing in a commercial environment and will need to drive actions in the retail environment.
At dunnhumby you won’t be only a data scientist. You’ll be part of a culture that welcomes participation and discussion at all levels. You’ll be able to see your impact in what we deliver day-to-day to our clients. You’ll also have the opportunity to work globally with assignments available in over 30 markets.
Leverage state-of-the-art Big Data tools (such as Hadoop, Spark, Python, R) and techniques to build innovative solutions using appropriate modelling techniques and data sets with a focus on implementation.
Build strong working relationships with senior research and applied data scientists and client leads to ensure full understanding of client problems and context behind them before starting any project.
Adhere to all key aspects of effective project management right from the scoping through to actual delivery to clients ensuring there is clear and effective communication on progress or challenges at every stage.
Interpret results of the data models and create deliverables that explains the approach and results in simple terms that clients can understand and act on.
Work closely with research data scientists to pick the relevant analytical techniques and models that best address client’s questions after carefully understanding the merits and limitations of the various approaches.
Document new learnings and improve existing ones after deploying solutions for client to capture feedback on the methodology post actual implementation to augment the existing knowledge repository.
Ensure all analysis and data modeling is completed on time and with high accuracy and quality after being carefully QA’d and reviewed by senior applied data scientists.
Participate in in-person client meetings along with senior data scientists and client leads to contribute towards the socialization process of the methodology and solutions.
Identify opportunities for standardization & further automation of existing solutions and processes to free up time to work on new challenges for the clients.
Start contributing ideas and sharing client learnings with the research and applied data science community to improve existing solutions or models.
Experience with NLP (Natual Language Processing) is a must for this role
BSc in Mathematics, Economics, Applied Statistics, Computer Science, Physics, Engineering or related field
Experience in data analysis or data mining preferably with SQL, Python, R or similar
Good ability to interpret business requirements, translate into data science problems and deliver high value outputs.
Ability to work quickly and iterate through trial-and-error, rather than spending an excessive amount of time designing the most robust methodology to deploy at the outset
Ability to make data tell a story through data presentation / visualisation tools and techniques
Masters in Data Science, Applied Statistics, Behavioural Economics, Computer Science, Artificial Intelligence, Machine Learning, Big Data, Physics, Engineering or related field
Experience with retailers’ data would be useful but is not essential.
Proven track record of applying advanced statistical models and machine learning algorithms to solve a variety of complex problems
Practical experience in relevant machine learning and data modelling techniques used in supervised problems such as decision trees, Random Forests, Gradient boosted machines, linear/logistic regression, deep learning or unsupervised problems such as clustering and dimensionality reduction.
Spark, Hadoop and cluster technologies.
How to apply:
Please mention NLP People as a source when applying
dunnhumby, a customer science company, helps retailers and brands analyze data in order to improve customer experiences and build loyalty.