Head - Data Science - BFS

Client is looking for a phenomenal hands-on Head of Data Science to analyse massive sets of data, generate powerful insights, and create data products which directly inform our daily decisions on growth, retention, revenue, merchandising, new categories, operational efficiencies, and consumer experiences. The Data Science department will apply quantitative analysis, data mining, and the presentation of data to guide and steer the team's efforts to convey key product trends and opportunities. Ultimately, you will lead (and grow) the team to develop machine-learning algorithms to personalize user experience, product recommendations, and churn intervention.

Roles and Responsibilities

- Set the vision, create a roadmap, and maintain (and invest) in infrastructure-team-process.

- Set the culture and mission to attract the best team possible. Continuously refine the set of priorities for a team of Data Scientists, Data Engineers and Analytics Managers.

- Oversee the development of the technology stack that will enable data exploration and analysis including: data architecture, tagging and operational processes, data taxonomy, and reporting.

- Work with all stakeholders (marketing, operations, merchandising, finance, product design, etc.) by gathering data from all business units, developing requirements, ascertaining priorities, and reporting progress

Qualifications

- Min 10 + years of expertise working on and managing analytics/data science teams with consumer-facing companies (ideally in the eCommerce and/or subscription space).

- Ability to both manage and recruit a team while still being hands-on.

- Fluency in R, Python, or Julia.

- Experience with relational databases / SQL.

- Experience using Dynamo, Cassandra, Hbase, or other non-relational DB.

- High skill in data visualization.

- Proven ability to set a vision of where we will be in 2-5 years and set in place the systems-level thinking to get there.

- General industry knowledge of how distributed database infrastructure has been the solution to handling some of the biggest data warehouses on the planet - i.e. the likes of Netflix, Google, Amazon, Facebook, LinkedIn, and Twitter.

- Solid understanding of the Data Scientist project lifecycle processes including: initiation, identification of data needs, methodology selection, proof of concept, release and version control, validation and experimentation, production releases, maintenance and iteration.

- Deep understanding how to extract data from homogeneous or heterogeneous data sources (ETL), and transform the data