Senior Data Scientist

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An boutique firm that provides analytically-driven consumer insight services to a broad spectrum of consultancies and research agencies, as well as working directly with some of the world's leading brands.

Whether conducting advanced analytics or delivering full market research programmes, this company consistently delivers creative solutions to help clients solve business issues and make better-informed decisions.

This company has four core specialisms: predictive models; segmentation; brand strategy; and product & service optimisation. Customer analytics will play a key role in all of these areas.

The Role

They are looking for an experienced, commercially-focused, and passionate leader who will grow and manage the new customer analytics team as a senior data scientist.

You will be building analytics models using a range of data science approaches, such as machine learning and statistical techniques, along with wider analytical approaches such as time series and analytics with unstructured data e.g. NLP. They recently used gradient boosting machine learning to predict radio listening behaviour for a client, taking into account advertising spend.

The Successful Candidate Is Likely To Have:

• Ability to talk confidently to clients, discussing and advising on business objectives
• Proven track record of how results can best be deployed within businesses
• Ability to take a flexible approach to workload, to work autonomously when required, demonstrating the ability to prioritise and organise

Technical skills:

• Excellent working knowledge of advanced analytical techniques, including regression, segmentation, machine learning, natural language processing, text analytics, and time-series modelling
• Experience of a wide variety of technologies including: R or Python, SQL
• Understanding and experience with cloud infrastructures such as Azure or AWS and how to integrate R or Python analytical workflows would be highly desirable
• Proven track record with accessing client customer databases and data lakes
• Knowledge of the major secondary data-sets available, and experience of merging these to create additional insights
• Experience with web scraping tools and techniques

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