Movable Ink
We are looking for a Principal Data Scientist who will work with our Product and Engineering teams to create artificial intelligence based recommendations and insights extracted from modeling and analysis of very large sets including Movable Ink’s historical data. The ideal candidate is an expert at using large datasets of various kinds and presenting insights from those datasets to senior leaders. This data may include images, text and hypertext to find opportunities for computer vision, natural language processing. This candidate should have a passion for discovering solutions hidden in large data sets and working with stakeholders to create value for our clients.
Responsibilities: Lead the hands on technical management of production Machine Learning models for propensity scores, creative attribute rankings and recommendations Develop new ideas with the AI team and develop the most impactful models that deliver value to customers. Deliver strategy recommendations to internal stakeholders for maximizing customer success in using AI Development of performance based score models using comprehensive set of features Create and enhance bayesian optimization algorithms and test setups for marketing campaigns Collaboration with various stakeholders throughout the organization to identify opportunities for leveraging domain knowledge and data to create new features into the Artificial Intelligence based products Mine and analyze data from a data warehouse to drive improvements in product development and understanding of why AI based marketing campaigns drove lift. Assess the effectiveness and accuracy of new data sources and data gathering techniques Develop processes and tools to monitor and analyze model performance and data accuracy Partner with key stakeholders on the ML Engineering team in the rollout of new models Qualifications: 7 years of experience manipulating data sets and building statistical models, education in a quantitative field or equivalent working experience Strong problem solving skills with an emphasis on statistical model development, including bayesian methods and causal inference Experience using statistical programming languages (Python, Spark, SQL) to manipulate data and draw insights from large data sets Knowledge of a variety of machine learning techniques (clustering, decision tree learning artificial neural networks, etc.) and their real-world advantages/drawbacks Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, bayesian probabilities, etc.) and experience with popular machine learning libraries such as TensorFlow, PyTorch, scikit-learn and scikit-image A drive to learn and master new technologies and techniques Experience using Google services: BigQuery, Cloud Storage, Vertex AI, Dataproc J-18808-Ljbffr
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