Data Scientist

Job Description

Job PurposeThe Data Scientist will support our product, sales, leadership and marketing teams with insights gained from analyzing company data. The ideal candidate is adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action. They must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations. They must have a proven ability to drive business results with their data-based insights. They must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.Responsibilities

• Guide and inspire the organization about the business potential and strategy of artificial intelligence (AI)/data science
• Identify data-driven/ML business opportunities
• Collaborate across the business to understand IT and business constraints
• Prioritize, scope and manage data science projects and the corresponding key performance indicators (KPIs) for success
• (Help to) define and communicate governance principles
• Understand new data sources and process pipelines, and catalog/document them
• Acquire access to various databases, and other source systems such as SQL or graph databases
• (Help to) create data pipelines for more efficient and repeatable data science projects
• Apply statistical analysis and visualization techniques to various data, such as hierarchical clustering, T-distributed Stochastic Neighbor Embedding (t-SNE), principal components analysis (PCA)
• Generate hypotheses about the underlying mechanics of the business process
• Test hypotheses using various quantitative methods
• Display drive and curiosity to understand the business process to its core
• Network with domain experts to better understand the business mechanics that generated the data
• Apply various ML and advanced analytics techniques to perform classification or prediction tasks
• Integrate domain knowledge into the ML solution; for example, from an understanding of financial risk, customer journey, quality prediction, sales, marketing
• Testing of ML models, such as cross-validation, A/B testing, bias and fairness
• Collaborate with ML operations (MLOps), data engineers, and IT to evaluate and implement ML deployment options
• (Help to) integrate model performance management tools into the current business infrastructure
• (Help to) implement champion/challenger test (A/B tests) on production systems
• Continuously monitor execution and health of production ML models
• Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.
• Develop custom data models and algorithms to apply to data sets.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.

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