Main Purpose of the Job
A Data Scientist utilizes his/her analytical, statistical, and programming skills to understand business questions and challenges across Nestlé. S/He then develops statistical and analytical models to address these challenges using diagnostic, prescriptive, and predictive techniques by collecting, analyzing, and interpreting large data sets.

To build these models, s/he may engage scripting experts or perform scripting independently. S/He also formulates testing strategies and validates hypotheses in an effort to establish viable use cases, completing these phases using Agile methodologies.

If the business case is proven, s/he collaborates with Solution Architects or Data Engineers to embed value-generating and successful models into operations and help design them as key components of industrialized solutions. S/He is also expected to drive analytical maturity by engaging executives and senior stakeholders and providing mission-critical insights to key business and internal stakeholders.

Data Scientists should possess strong skills in data preparation, report writing, statistical modeling, visual exploration, and insights generation, and should continuously strive to assess the business value of every initiative.

Key Outputs

Contribution to IT Strategy by facilitating exploration through POC/POV — under the supervision and guidance of his/her primary Community of Practice Lead and Product Group Manager based in Switzerland

• Articulates a vision and roadmap for the exploitation of data as a valued corporate asset in alignment with existing functional priorities, helping Product Managers explore new ways to solve complex business problems
• Determines data requirements needed to train and develop models and algorithms
• As part of a POC/POV, prepares data, develops variables and models, and tests, validates, and builds data-driven approaches to answer business questions
• Proves or invalidates hypotheses to identify hidden relationships and develop new analytical methods that may later become candidates for industrialization
• Works with lead markets, functions, and GMB/RMB to conduct the POC/POV and bring it to closure

Operational Effectiveness and Efficiency by helping industrialize proven models

• Uses predictive modeling to enhance and optimize customer experience, revenue generation, ad targeting, and other business outcomes by supporting product teams in industrializing models proven during the POC/POV
• Assists Solution Architects in developing processes and tools to continuously monitor and analyze model performance and data accuracy
• Supports Solution Architects in devising data collection procedures that include relevant information for building analytic systems
• Helps design better descriptive and prescriptive analytics solutions by visualizing information and developing reports on data analysis results to facilitate new KPI/PPI discussions
• Performs ad hoc analyses and presents results in a concise, executive-ready manner, even after industrialization
• Promotes the use of services rather than full automation where manual intervention is more appropriate based on cost-benefit analysis

Stakeholder Engagement

• Collaborates with stakeholders across the organization to identify opportunities for leveraging company data to drive business solutions
• Influences product teams through the presentation of data-driven recommendations
• Shares best practices with analytics and product teams

Key Experiences

• Master’s degree or PhD in Statistics, Computer Science, or a related field
• 5+ years of experience in an applied analytics environment
• Experience in developing and applying advanced machine learning algorithms and statistical techniques such as regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
• Experience using statistics and machine learning to solve complex business problems
• Experience with data visualization techniques and statistical programming tools such as MS Power BI, R, Shiny, and information modeling using SQL
• Experience using iterative advanced analytics project lifecycle methodologies such as CRISP-DM
• Excellent written and verbal communication skills, with experience coordinating across teams
• Demonstrated ability to communicate complex results and insights to both technical and non-technical audiences
• Demonstrated ability to work with minimal supervision
• Strong problem-solving skills with an emphasis on product development
• Preferably experienced in working in a global environment and with virtual teams