Background & Context Philip Morris International (PMI) has made a dramatic and historical decision: we are building our future on smoke-free products called Reduced Risk Products (RRP). Our vision is that these products will ultimately replace cigarettes. As smoke-free products are new in the industry, it requires us to change our business model, our operating model, our manufacturing processes and our consumer engagement. This will result in multiple categories, new product lines, new manufacturing processes, as well as new and direct interactions with consumers collected in an avalanche of data. We are switching from a B2B model to a mixed B2C one. Our product revolution has been the trigger point to embark into a complete transformation of our company and of our ways of working. It has also been the trigger point to shift PMI towards a data driven organization. We have only started PMI data and analytics journey in January 2017 by establishing an Enterprise Analytics and Data team (EAD) lead by Head of Enterprise Analytics & Data. Today we have close to 100 resources. EAD is a part of a newly established PMI Digital function. Our approach is to build simultaneously internal Data Governance and AnalyticsData Science capability. We believe that a simultaneous defensive and offensive data strategy is crucial for our success. EAD’s immediate objective is to accelerate RRP sales through actionable insights based on advanced analytics and a solid PMI-wide foundation of data. PMI Data Science organization today is about 50 people (including 35+ data scientists, 9 data science leads). There are four Data Science Labs in Lausanne, Krakow, Amsterdam and Tokyo. More than 30+ Data Science Use Cases were delivered so far. Data Science processes, tools and labs management is established as a good foundation for fast delivery. But there is still a lot to be done to fulfill our vision – “every decision, every day, driven by data.” The candidate will report to Head of Enterprise Analytics & Data and will work with close collaboration with other EAD leaders (Director Strategy & Planning Director Data Management Director Data Architecture and Director Business Intelligence). Purpose of the job Grow and develop the Enterprise Data Science organizational capability within PMI. Deliver high value analytics use cases to increase RRP sales together with Business Intelligence by leveraging predictive and prescriptive analytics. Challenges and Opportunities The key challenge is to a continuous interest and appetite for data-driven insights while balancing internal disruption which EAD and mainly PMI Data Science Team causes. Insights generated from data challenge conventional wisdom people rely on today. The story told by data may differ from the hypothesis people would like to formulate. This generates fear, resistance, misunderstanding and blockages. This requires strong business acumen, understanding of change management in corporate environment, ability to build trust and credibility from business leads. That is why Data Science Team must deliver actionable and trusted in-sights on high priority data science use cases and present them in an impactful way. Data Science team is a virtual one working in parallel in multidisciplinary teams. Ability to build and lead high performing and cohesive teams is a crucial leadership competence we expect from the candidate. This must be combined with ability to drive development of data science workforce competencies and skills to fit PMI evolving business needs. Data Science Delivery is dependent on multidisciplinary teams. The working relationships and collaboration with other EADDigital teams, IS, Functional counterparts is a key prerequisite for the success in the role. Critical Accountabilities Deliver Data Science Use Cases: Build Enterprise Data Science as a customer centric service delivering relevant and actionable insights for selected high priority use cases throughout data science methods. Facilitate hypothesis creation together with the business counterparts. Automate decision making throughout industrialization of data science use cases. Integrate Data Science and Business Intelligence (BI): establish appropriate processresources touchpoints between Data Science and BI teams for a smooth end to end analytical experience delivered to EAD customers Build Data Science demand through customer satisfaction: a constant appetite and understanding of insights built on data science, set the right and realistic expectations, deliver insights leveraging data visualization and data story telling in a language of EAD customers Build cohesive and high performing teams: strong morale and spirit in Data Science virtual team couch, drive and align Data Science Leads ensure clear work allocation and transparency of progress across whole team engage the team into mutual learning and exchange of best practices integrate newcomers ensure open dialog Develop Internal PMI Data Science Capability: Build & develop well balanced team of internal and external resources capable to react on fluctuating demand of data science use cases. Drive and evolve talent acquisition process to source required expertise. Translate PMI data science needs into an organizational development plan covering the design & composition of the team, behavioral and functionalskills development and individual development plans. Critical Experience Extensive relevant business experience. Proven experience in a lead advanced analytics role in multinational environment and scope. Prior experience in business intelligence, market research, and business analytics, strategic planning, preferred. Excellent understanding and practical experience in using data science methods, principles and needed capabilities. Comfort and ability to deal with ISIT related matters. Businessmarket experience and CPGFMCGRetail industry knowledge is preferred. Prior experience in the leading and growing of an enterprise wide data science organization is a must. Skills & Competencies Business Acumen and Managerial Courage – ability to understand various business functions, cross-functional interdependencies, business KPIs. Ability to ask the right questions, formulate problem statement and outcome of data science according to audience. Confident to follow up, raise and challenge senior leaders. Building performing teams and developing others – ability to lead, organize, grow and develop the team of different functional and technical experts. Lean Start Up Enterprise and Agile Methodologies – understanding of the methodologies and ability to practically apply them Learning Agility and Dealing with Ambiguity - ability to quickly learnadapt to a new environment, new projects, new activities Drive and Passion for Results – ability to resolve matters till the end, at high speed and qualityApply now
