BNZSA is a leading European marketing agency specialising in tele-based B2B demand generation for many of the world’s leading IT companies.
We are preparing the foundations to take BNZSA to the next level and are now looking for a Data Scientist to join our Data Team. Do you want to take this challenge with us?
This exciting opportunity for a Data Scientist will have you helping to shape data, owning and presenting your own work demonstrating transformational insight to our business and our client’s businesses – and pushing the boundaries of data being at the core of our initiatives.
Our aim is to use the wealth of data that we have and that our clients have, to create predictions, insights and recommendations. This starts with an exciting project to predict opportunity in our base, and will include bespoke work for our clients based on their 1P data.
What You’ll Do
Build analytical products
You will be responsible, in collaboration with the Head of Insights and the Data Engineers, for building a range of products that will enable us to understand customers, prospects and the changing market; to predict next best actions and identify opportunities; to plan and optimize the customer journey, among others. This role will require a combination of creativity, analytical thinking, and pragmatism to make best use of the data we have access to.
Support our teams
Our callers and agents rely on quality data to optimize the conversations they have with clients, customers and prospects. You will help them by enhancing the data they already have, producing derived insights, and give them the information they need to encourage engagement and conversion.
Support our clients
Our clients hold a wealth of historic 1P data. You will be involved in unlocking the potential of their data, identifying cross-sell and up-sell opportunities among existing customers, and supporting them with insights about customer behavior, journeys and next best experiences.
This role requires a good understanding of all phases of the analytic process including data collection, preparation, modelling, evaluation, and insight generation. The creative use of data/data sources is a must; you should have not only a strong analytical mind but also to be resourceful when it comes to solving problems with data.
You will be working with data engineers, who will support you in shaping and manipulating the data for you to use. You must therefore be collaborative and clear in your instructions, and capable of producing briefs that will support these engineers in their work. You will also be expected to present your work to non-technical audiences, stressing the business benefits and recommendations.
You will demonstrate a high level of expertise in the use of machine learning and statistical techniques. Skills in deep learning, data mining, knowledge extraction and graph theory are also highly desirable.
Experience working on B2B clients, tech, telecommunications and IT would be advantageous but not all essential.
What You’ll Bring
· Experience with both descriptive and predictive analytic techniques including clustering, regression, classification methods, etc. (Data Mining /Machine Learning);
· Data extraction from structured/unstructured sources; extracts data from API’s across a range of social platforms – JSON, XML;
· Good data manipulation skills using SQL based databases;
· Experience building customer-level marketing and analytic tools including customer segmentation and performing predictive modelling;
· Experience with data visualization and analytic techniques using tools such as Python/R (for visualization purposes), Tableau, Power BI;
· Understanding of Text Mining, Natural Language Processing;
· Place of residence must be located in the Comunidad de Madrid
Nice to have:
· Preference for candidates with experience working for B2B clients, especially in IT / tech / telecoms;
· Experience working with Big Data, cloud based and open source technologies including Apache Hadoop, Amazon Web Services and/or ideally use of Google Big Query and Google Cloud Platform (preferred);
· Use of NoSQL technologies like MongoDB or Graph Databases (e.g. Neo4J)