Full/part time: Full-time
Reports to: Data and Analytics Market Lead
Start Date: ASAP
UL is driving digital transformation deliver 4G growth. Our mission is to transform our organization to a digital first culture, delivered by a world-class data and insights team, leaping ahead of our competition in everything we do.
UL’s D&A Market teams will lead the path for UL to become data-intelligent. The team will do this by delivering advanced products at scale, enabling UL functions, UniOps and leadership teams to turn data into insights, actions and decisions at a faster pace than ever before to achieve competitive edge for UL.
Each Market team consisting of technical (data engineering and science) expertise as well as functional business insight generation expertise (e.g. Finance, CMI, CD) will work with other Market teams and the D&A Global team to ensure that insights discovered locally are joined up and enhanced with regional/globally-discovered or researched insights to enable joined-up recommendations and decisions aimed at driving incremental growth by meeting the needs of our consumers, customers and employees.
With the ULE looking to D&A Market teams to power the exponential growth of UL in an in increasingly VUCA (volatile, uncertain, complex and ambiguous) context, it is imperative that D&A Market teams are organised to be highly adaptive and flexible to demand, fast-changing priorities and emergent opportunities. To do this, it will be important for the D&A Market team to have an agile mindset, org structure and ways of working (beyond agile tools and techniques) – i.e. able to embrace uncertainty, ambiguity, and to create structure out of chaos.
There are five major themes in the Data and Analytics global strategy that will relate to the work of D&A Market teams:
1. 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.
2. Winning disproportionately. Creating analytics products that make a significant impact on our business and drive competitive advantage in the market, while collaborating appropriately with other markets and Global teams (i.e. winning both locally and globally for UL).
3. 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.
4. 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.
5. 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
This team is responsible for managing the market’s demand for data, analytics and insights. It will lead data analytics, data warehousing, research development and implementation of appropriate data systems and analytic solutions. It is also responsible for streamlining in-scope processes related to data analytics and insights generation in the market, through automating, consolidating and eliminating them where appropriate to deliver a seamless, fast insights-generation experience using tooling provided by the Global D&A team.
We are looking for an experiences Data Scientist help to create competitive advantage for UL through assisted and predicted decision making. The role works closely with the NAMETRUB D&A Team and Global D&A Data science organisation.
MAIN JOB PURPOSE:
You are a data scientist with a passion for data, data science, AI and ML, demonstrating understanding of advanced data science models and their application into the business. This role will focus on understanding and supporting the growth of the Business, building advanced and repeatable data science models that enable the business to make better decisions, drive 4G, and gain competitive advantage.
1. Understand the business problems and how to deliver relevant insights that lead to actions
2. Gather data and build data-science and algorithmic solutions to address business problems requiring descriptive, diagnostic, predictive, and / or prescriptive analytics
3. Create experimentation to solve complex business problems and deliver predictions on future business outcomes in a repeatable and relevant way
4. Support product teams scaling models into low-touch solutions to provide optimal ROI from data science
5. Work on in-market business problems coming from CCBTs, CD, Finance and CLT. Identify common themes to build repeatable models and partner with CD, CCBT, Finance leadership to support 4G growth though data and analytics, and / or advance the next phase of NRM
6. Position analytics as a tradable “currency” with customers to gain competitive advantage
7. Drive new value from insights from connecting external and internal data sources
8. Innovate new data and analytic methodologies driven by local needs which feed into D&A’s global product pipeline
EXPERIENCE, QUALIFICATIONS AND QUALITIES:
• B.S. or M.S. in a relevant technical field (Operations Research, Computer Science, Statistics, Engineering, or Mathematics)
• 1-4+ years’ work experience in a data science role with significant focus on large scale and/or unstructured data
• Experience managing projects from start to finish
• 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
• Strong communication skills, particularly the ability to communicate complex quantitative insights in a precise, and actionable manner to business leaders
• Strong track record in solving analytical problems using quantitative and machine learning approaches
• Working knowledge in common machine learning techniques such as Random Forests, Boosting, Regularized Regression, Naïve Bayes Classifiers
• Working knowledge of advanced machine learning such as Deep Neural Networks, Support vector machines, Reinforcement learning and Bayesian networks
• Working knowledge in classical statistics (Regression, Clustering, Optimization, Time Series, Probability)
• Deep experience in testing and measurement (A/B, multivariate, inferential measurement e.g. CausalImpact)
• Deep experience working with and coding in R, R Shiny, Python
• Working knowledge of data visualization concepts in reports (Power BI) and specialist tools (D3 or equivalent)
• Knowledge of extracting and combining complex, high-volume, high-dimensionality data from multiple sources (enterprise, proprietary, IoT, public domain), including unstructured data (comment threads, audio, video)
• Working knowledge working with large data sets, experience working with distributed computing tools a plus (Apache Spark, Hive, Impala)
• Working knowledge working in Microsoft Azure and scaling analytic products over GPUs in the cloud
• Possesses a natural curiosity, openness to possibilities and imagination to create novel business solutions
• Ability to work collaboratively with peers and demonstrate vertical and lateral influence.
• Machine learning forecasting techniques Fully Operational
• Statistical modelling Fully Operational
• Operational research and supply chain Working Knowledge
• Optimisation techniques and tools Fully Operational
• Manipulating multi-source data Fully Operational
• Python coding Fully Operational
• Cloud architecture (preferably MS Azure) Working Knowledge
• Simulation packages e.g. Anylogic Working Knowledge
• Distributed computing (Hadoop, Spark) Working Knowledge
• Project Management Working Knowledge
• Communication / presentation skills Working Knowledge
• Agile methodology Working Knowledge
Standards of Leadership
• Personal Mastery (Data-science and advanced analytics)
• Passion for High Performance
• Business Acumen
• Business Acumen
• Data Science
• Machine Learning
• Statistical Analysis
• Data Analytics
• Data Expertise
• NLP / NLG
• Model re-engineering
• Model Deployment
• Machine Learning
• Cloud programming
• Data warehouse, Data lakes
• Market LT
Market D&A Team
• Global D&A Team
• Unilever Business leaders and subject matter experts
• Global business topic Subject Matter Experts
• D&A Market Team and other such teams across UL
• CD Directors
• CCBTs key leaders and team members
• Analytics and Data Science product peers
• Partner Analytics and Data Science Contractors
• Targeted Universities for developing innovations
• Data Science Interest Groups