EVP - Data Science - Big Data Firm

Job Description

About Us

Goals101 is a one of the fastest growing big-data platforms in the region, wherein its Technology Platform is driven by Deep Insights derived from Purchase Behavior. The Proprietary Engine of the Platform is powered by ML, Deep Learning & AI, designed by some of the leading scientists across the globe. The company leverages on multiple data sources across industries, so as to add value to its banking partners & drive ROI for them, marketers and other partners in the value chain. The venture is backed by serial entrepreneurs, senior bankers, product heads, data scientists, ad-tech & digital experts of leading companies, some of whom are noted alumni of Harvard Business School, IIM-A, IIT, leaders of Fortune 50 companies and others. Over last few month's Goals101 has partnered with the leading 17 Banks of Asia. It now has operations across 14 countries.

Website: http://www.goals101.in

Headquarters: Delhi

Year Founded: 2016

Company Type: Privately Held

Company Size: 51-200 employees

Specialties: Fintech, Machine Learning, Artificial Intelligence, Big Data, Market Research, and Ad Tech

Overall Job Purpose

Goals101 is looking for a phenomenal 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.

Responsibilities

- Set the vision, create the 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 technologies employed.

Requirements

- 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.

- 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 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.