Jostens is looking to understand consumer behaviors through advanced analytic models to improve member engagement strategies.
Requirements
- Statistical modelling and advanced machine learning techniques, including supervised and unsupervised methods
- experience in cloud-based ML/AI technologies – preferably in the Snowflake and AWS environments
- programming experience in Python and/or R (both preferred) and SQL
- experience using big data (billion+ rows) across numerous systems and platforms, specifically in spotting and fixing standard data anomalies seen at this scale
- experience in presenting & communicating complex projects to non-technical teams.
- Understanding of big data principles and technologies with working knowledge of data integration using established methodologies and technologies.
- Continuous Integration and Delivery (CI/CD) experience, preferably in Snowflake/AWS environments.
Responsibilities
- Support. Develop presentations around insights and tell compelling stories with the data, deliver results to constituents at different levels: strategic, tactical, operational and across business units (Yearbook, Scholastic, College).
- Update and Maintain. Integrate and prepare large, varied datasets, both internal consumer behaviors as well as external and 3 rd party data sources such as consumer demographics, to gain a holistic understanding of consumer behaviors and preferences.
- Develop. Build and maintain predictive and machine learning models by using techniques such as logistic regression, gradient boosted trees, random forest and recurrent neural networks. Build and maintain unsupervised learning algorithms such as consumer segmentations using techniques such as k-means and KNN. Create and maintain recommender systems such as next best action, product affinities, likeliness of upsell at checkout, etc. Develop and maintain systems to continuously monitor model performance, such as model and feature drift.
- Advise. Oversee the implementation of the models into production AWS/Snowflake environments – ensure models are scoring properly.
- Strengthen. Analyze complex consumer data such as purchase transactions, demographic/psychographics, website behavior and email marketing data. Dig beneath the surface of a problem to ascertain the “Why” in the backend data, uncover the true reasons data patterns and skews may be showing up, and convert those findings into clearly understandable, data-backed recommendations, to influence future member engagement strategies.
- Help. Recommend campaign test designs and experiments to test hypotheses, such as A/B testing, champion/challenger testing, etc. Collaborate with the data engineering team to gather the right data from the right systems, as well as make recommendations for continuous data improvement and cleanliness pipelines.
Other
- At least 3 years’ relevant experience
- Undergraduate degree in Computer Science, Machine Learning, Applied Statistics, Mathematics or a relevant Engineering discipline. Preferred masters level degree in a quantitative discipline such as computer science, mathematics, machine learning, applied statistics or relevant engineering discipline.
- Ability to balance hands-on coding and modelling (existing and new models) on a frequent basis with being able to summarize findings and present insights to non-technical teams.
- Team player with a collaborative mindset, always prioritizing impact for the business.
- Innovative mindset – relentless curiosity of big data and its ability to predict consumer behavior.
