Data Scientist (3-6 yrs)

Job Title : Data Scientist

Job Purpose :

This purpose of the role is to work on advanced research and development, to enhance relationships with clients, acquire strategic datasets and provide new and innovative solutions via product prototyping. The role holder will concentrate on new data asset evaluation and acquisition as well as new product prototyping. This will involve inventing and modifying advanced innovative algorithms and applying them to large data sets to solve difficult business problems. The role holder must be able to balance and prioritise multiple concurrent projects, anticipate obstacles, and make high quality deliveries on an aggressive schedule.

Principle responsibilities :

Data Analysis :

- Analyzing, processing, evaluating and documenting large data sets

- Identify and apply appropriate machine learning/data mining/text mining techniques to uncover the value of the data

- Designing data structure and data storage schemes for efficient data manipulation and information retrieval Developing tools for data processing and information retrieval

- Developing data driven models to quantify the value of a given data set

- Validating score performance

- Conducting ROI and benefit analysis

- Documenting and presenting model process and model performance

- Analytical and industry knowledge applies to construct analytical solutions with details for internal and external clients to solve their business problems.

- Makes independent decisions that require developing new options and troubleshooting uncertainty to solve complex problems.

- Time Taken: 40%

Problem Solving and Innovation :

- Develop best practices for utilizing Experian and client data and producing new insights

- Develop appropriate business assumptions for developing relevant solutions

- Develop state-of-the-art topical material for Knowledge Share and relevant Roundtable discussions

- Time Taken: 25%

Project Management :

- Works independently on assignments using knowledge and work management to address issues within project team.

- Gathers input from colleagues and

- Influences at the multiple account and project level internal and external, and may lead project and account team Time Taken: 10%

- Managing Client and Internal Relationship

- Lead and advocate efforts to improve client's analytical solution, data quality, model fit, and project timeline Lead team efforts for finding optimal solutions

- Time Taken: 10%

Teamwork and KS :

- Shares expertise with others and continually strive to improve overall contributions to the department. Provides informal and formal mentoring within project team

- Become an analytics advocate through publication and internal and external presentations

- Actively participate in the greater analytical community through conference presentation and white paper.

- Time Taken: 5%

Personal Development :

- Be connected to clients, competitors, relevant market groups, to be aware of and understand movements, trends, up-coming opportunities and risks.

- In consultation with line manager, maintain and action agreed personal development plan.

- Seek feedback to improve skills, knowledge and competencies as part of the feedback process.

- Actively seek to develop competency/knowledge of both software and analytics (Extensive understanding)

- Keeps abreast with recent developments in Experian data assets, analytical methods and software tools through internal and external training.

- Show willing to take on additional responsibility and more difficult project work.

- Time Taken: 10%

Other responsibilities :

- Comply with company rules and procedures and stay informed about their updates

Measures of Performance :

- Data Analysis are delivered to budgeted time, spec, and with a high level of accuracy first time

- Data Analysis are delivered by following best practices and processes

- Documentation is in place and is meeting the project standards

- Team's innovation activities are supported

- Best practices and guidelines updated when necessary

- New methodologies and approaches are shared with the ACES team and broader analytics community NPS score of the projects being involved meet the team targets

- Own PDP targets are achieved

Required key skills (functional / technical) :

- Advanced Degree or equivalent in Machine Learning, Computer Science, Electrical Engineering, Physics, Statistics, Applied Math's or other quantitative fields

- Strong grasp of probability, statistical inference, optimization algorithms, linear algebra, and calculus.

- Understanding of how and when to apply key analytical approaches including regression analysis, machine learning, Al, deep learning and other advanced modeling approaches to extract and automate insights from data.

- Strong grasp of techniques for model cross-validation and penalizing unnecessary complexity, including regularization, Bayesian shrinkage, in-sample vs out-of-sample. Fluency with statistical significance tests and Monte Carlo simulation.

- Ability to write near-production-level code in at least one general purpose programming language (e.g. Python, C++).

- Understanding of modern technologies in processing of large data sets, including Apache family (Hadoop, Spark), latest database technologies (e.g. Graph databases, time-series databases), and natural language processing and other tools for the parsing of unstructured data.

- Good communication skills