Principal Data Scientist
Experian DataLabs (Location, london or nationwide)
The Experian DataLabs UK&I and EMEA are based in London. (Although our teams are working from home) The team is dedicated to advanced research and development, and benefits from the mentality and drive of a start-up, combined with the backing of a resourceful corporation. This environment encourages innovation by supporting ambitious, rewarding and risky projects, even if they're likely to fail.
You will be part of the Experian DataLabs UK&I and EMEA. This is a primarily hands-on position where you will be empowered to be ambitious and bold, using whatever tools and methodologies to contribute in developing models and solutions that solve challenging problems and have the potential to directly and substantially impact Experian's future. The position may also require mentoring/management of junior members of the team.
The role requires that you have an extensive background in machine learning and data mining. A proven track record in inventing and modifying advanced innovative algorithms and applying them to large data sets to solve difficult business problems, is essential. You will be a team player who is eager to both teach and learn on a daily basis, who is proactive and self-motivated and has excellent communication skills.
To understand more about our work, please consult our , and some of our incubated products: , and .
Key job functions
- Applying, modifying and inventing algorithms to solve challenging business and technical problems. Developing tools for data processing and information retrieval.
- Developing models to quantify the value of given data sets.
- Validating model score and performance.
- Documenting and presenting model process and model performance.
- Analyzing, processing, evaluating and documenting large data sets.
- Designing appropriate data structure and data storage schemes for efficient data manipulation and information retrieval.
- Conducting ROI and benefit analysis.
- Engage and influence business leaders in solution path design.
- Mentoring/managing junior members of the team, if appropriate.
- Champion a culture where the fair treatment of customers is at the heart of the Experian business.
Could this role be for you?
Desired Skills and Experience
We are looking for ambitious scientists with an exceptional academic background, an ideal blend of coding, machine learning and statistics, a colleague with whom we can share the enjoyment of being curious, the interest in difficult mathematical and algorithmic problems, and the drive to be innovative in building predictive models as well as in the way society deals with sensitive data.
We would like you to have:
- An advanced degree (PhD preferred) in Computer Science, Physics, Engineering, Mathematics or a similarly quantitative field.
- Multi-years (5+) postgraduate or industry experience in topics such as machine learning, data mining, analytics, and predictive modelling.
- A scientific mentality with the ability to ask the right questions, as well as answer them.
- A proven track record in developing, innovating, and applying advanced algorithms to address practical problems and in building new analytical products.
- Demonstrated ability to lead and execute projects from start to finish.
- Deep knowledge and experience with several learning techniques such as clustering, regression, neural networks, SVMs, trees, and ensemble methods such as random forests and boosting.
- Proficiency in Python and preferably in some of R, Scala, C/C++, Java, and Matlab.
It would be fantastic if you also have:
- Experience with horizontally scalable data stores such and technologies such as Map Reduce, Spark, Cassandra, Yarn.
- Demonstrated experience in engaging and influencing business leaders in solution path design.
- Experience in applying advanced algorithms and building analytical products in a commercial setting, ideally in industries such as finance, insurance, healthcare, energy or telecommunications.
- Worked with GPUs and have used CUDA programming.
Deep knowledge with graphical models, Bayesian networks, Gaussian processes, MCMC, hidden Markov models, causal inference, and social network analysis.
- Experience with deep learning techniques for text, voice processing and modelling.
- Applied agile methods for software development