Head - Data Science - Internet/Online

- 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.

- Build applications, both consumer-facing and internal, so that we can collect and analyse billions of real-time data points on our products, service, and customers and instantaneously optimize customer experience or resource utilization.

- Manage reports, create dashboards, and visualize data to communicate the delivery of information to stakeholders.

- Ensure all the three phases of ETL (extract, transform, load) execute in parallel and are managed seamlessly.

- Consider important KPIs and measurements including latency, concurrency, access pattern, queries, data scope, end users, and the technologies employed.

Qualifications:

- Min 7+ 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.

- General industry knowledge of how distributed database infrastructure has been the solution to handling some of the biggest data warehouses.

- 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 for storing it in the proper format or structure for the purposes of querying and analysis.

- Experience developing dashboards and key metrics to track the business and inform strategy.

- Comfort with ambiguity and constant change.

- A strong communication skill set to make sure your team understands the why behind what they are building as well as how they are going to measure to understand success.

Education : 

- UG: Any Graduate in Any Specialization

- PG: Any Postgraduate in Any Specialization

- Doctorate: Doctorate Not Required