Commercial Digital Data Analyst

Overview Location Overview Working with inspiring and experienced colleagues, you'll find that the atmosphere in our city-center office in Kraków is informal and engaging. With drive and ingenuity, our teams deliver vital services to PepsiCo employees around the world. With an active, get-things-done culture, this is a place where your dynamism and agility will make a difference. Job Overview The purpose of the role is to support our local ownership of advanced analytics models supporting digital ordering, engaging tool and any other digital capability under the DX umbrella. Produce, manipulate and interpret model outputs to facilitate use by local teams/tools. Support integration of model outputs into local business processes. Identify and escalate any issues with model performance. Identify and escalate opportunities to enhance and expand model functionality/ applications. Responsibilities Build & codify local digital ordering approach: collaborate with sector stakeholders to co-create capabilities, tools & processes to drive efficiency, effectiveness & sufficiency. Develop best-practices for digital ordering and scale lifting and shifting learnings across countries and categories Maintenance of existing models Periodic refresh of existing models across markets and brands Periodic model validation to check need for rebuild Guiding business with periodic updates Formulate hypotheses, plan and execute statistical models for measurements/ROI analyses, share results with key leaders as well as drive meta-learnings Ensuring Statistical robustness of the models and model reads Ensure timely build and refresh of the models so that it can help in timely business decisions Actively test data science concepts, and technologies that can be scaled across portfolio Partner with PepsiCo functional teams, agencies and third parties to ensure acquiring, tagging, cataloging and managing data periodically in structured format as needed for measurement statistical models Create reusable modeling assets(codes, techniques, functions) to cut short refresh and model build time Engage in R&D to try new modeling techniques to ensure faster and better answers to business problems Qualifications 2-5 years of hands-on data science, model building Experience either in analytics consulting or internal analytics teams Experience in translating business problem into analytics framework and vice versa ‘Best in class’ data science capabilities, for instance a Master’s degree or PhD in data science or maths. Advanced knowledge of key data science techniques: Combining data from multiple sources through APIs, Semantic Web, etc. Data preparation and feature engineering Supervised / Unsupervised learning Collaborative Filtering Location Analytics & Intelligence Hands-on knowledge of statistical modeling- well versed in techniques like regression, Bayesian, SEM etc. Hands on with Machine learning concepts & skills – Regularization, Gradient-based optimization Hyper-parameter tuning, models like Random Forest High level of proficiency in either R or Python, Azure, VBA, SQL, MS Excel Familiarity with query languages such as SQL or Hive Able to translate complex concepts into clear stories for the business, proven through at least 18 months experience in a business facing data science role. Knack for new age data platforms, understanding of relational as well as unstructured data, experience in data lake architecture and creation Problem solving aptitude Familiarity with Scala, Java or C++ is an asset