Data Science Manager

7 - 16 yrs exp
7 - 16 yrs exp
About opportunityAbout opportunity

Background: For Unilever to remain competitive in the future, the business needs to continue its path to become data intelligent. The Information and Analytics team is responsible for building data, data science and analytics as a core capability to help the business become data intelligent and drive business and organization performance. The team will deliver advanced products at scale and work closely with the market teams to ensure that decisions in Unilever are augmented with insight & recommendations wherever possible.

There are five major themes to the Information and Analytics strategy:

  • Deliver Intelligent systems at scale. Powering Business (cross functional) and Customer Development area through data and analytics. Building out the right applications to enable business teams to move faster, with more accurate and future looking decisions leveraging Big Data & Data Science
  • Winning disproportionately in Markets. We believe that the best way to move the needle for Unilever in Analytics is to work with leading geographies to create analytics products that make a significant impact on our business
  • Drive Unilever to assisted and predictive decision-making. The future is about assisted decision-making with machines (AI/Cognitive Computing/Machine Learning) unlocking the insights from vast deluge of consumer, customer & internal data, and presenting this in a way that is simple for our teams.
  • Make data a true asset. Better data and data science access for every part of Unilever, across the 3 data framework (Connectivity, Growth, Continuous Improvement) and leading through the Enterprise Data Executive.
  • World-class Information: One Version of the Facts. Continued excellence in delivery of diagnostics and insights to focus attention when and where it matters with a focus on Big Bets, Strategic Initiatives and Leadership team reporting

Main Purpose of the Job:

  • Consumers increasingly shop in an Omni channel environment in which online sales impact offline and vice versa. In this world, Unilever e-commerce sales will depend heavily on influencing shoppers to start shopping online (i.e., grow category penetration), shop more frequently, make larger purchases, and to buy from a broader array of Unilever categories. Rich e-com data gives vivid insights into shopper behaviour e.g. pages visited, other purchases, time spent on Web sites, cart abandonment. Self-ordering through IoT / Voice and newer fulfilment models like drone delivery, prescheduled delivery are adding even more complexity and richer data.
  • About half of all purchase decisions are digitally influenced. The ability to link any digital moment right through to purchase in the Connected World, means that data becomes key to unlocking new growth opportunities.
  • The objective of the Unilever eCommerce initiatives is to enable Unilever business teams to create a mutually profitable and high-growth business with ecommerce players.
  • This is an exciting new role in the Data Science and Analytics group in Information & Analytics, which is tasked to deliver maximum value from data & data science in the eCommerce Area.

Key accountabilities:

  • Apply data exploration and analysis techniques to examine clickstream, POS, and IOT data from multiple disparate sources, with the goal of improving customer understanding and providing a competitive advantage for Unilever Ecommerce business teams
  • Apply statistical and predictive modeling concepts, clustering and classification techniques, and recommendation algorithms to help optimize customer targeting, reduce churn, and increase customer life-time-value
  • Lay the groundwork for reinforcement learning systems for eventual operations optimizationExpected Work
  • You will help to expand our current analytic capabilities and architect new strategies and applications within a dynamic innovative organization. You will shape the future of what data-driven organizations look like, drive processes for extracting and using that data in creative ways, and create new lines of thinking within our ecommerce area.

Specifically, this role will focus on leading the development of data-science and algorithmic solutions that power Unilever’s global ecommerce solutions and business teams. The role will integrate design inputs provided the global Data Science COE and in-market data scientists working on eCommerce; and prioritize the most critical features into their models – ensuring a healthy balance between global scale and local relevancy.

Key Deliverables

  • Build data-science and algorithmic solutions to address business problems requiring descriptive, diagnostic, predictive, and / or prescriptive analytics
  • Perform exploratory and targeted data analysis using descriptive statistics and other methods
  • Drive new value from insights from connecting external and internal data sources
  • Build learning systems to analyze and filter continuous data flows and offline data analysis

Experience and qualifications required:

  • B.S. or M.S. in a relevant technical field (Operations Research, Computer Science, Statistics, Business Analytics, Econometrics, or Mathematics). Analytical Development experience of 7+ years preferred.
  • Experience in ‘digital native’ company applying advanced analytics with clear vision for applying previous experience in consumer goods industry. Prior experience with Ecommerce space is a strong plus.
  • Experience with digital analytics (Next Product to buy, cross channel attribution modeling, digital analytic tools, dynamic ecommerce pricing, web scraping etc).
  • Strong track record in solving analytical problems using quantitative and statistical approaches
  • Expert knowledge in statistics (Regression, Clustering, Random Forrest, Decision Trees, Optimization, Time Series, Probability, and other related advanced methodologies)
  • Adept in selecting features and optimising classifiers.
  • Good knowledge of an analysis tool such as Angoss, KNIME, Microsoft PowerBI etc
  • Expert knowledge working with and coding in R and Microsoft Azure Machine Learning
  • Experience in Machine Learning Toolkits (Tensor Flow, Caffee) is a plus
  • Passion for empirical research and for answering hard questions with data
  • Ability to apply an agile analytic approach that allows for results at varying levels of precision
  • Ability to communicate complex quantitative insights in a precise, and actionable manner
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