About the Role
The Senior Data Scientist will have functional expertise in data science and machine learning, leading the charge on innovative work, mentoring more junior data science specialists, and guiding the organization on methodology and best practices. Working closely with our compliance, marketing, and the larger data teams, you’ll dive into a powerful data set to help us structure analysis and develop insights that will help to answer important questions.
• As part of the data team, contribute to solving business problems by framing the problems, determining intended approach and statistical/quantitative methods, evaluating the analytical solutions to the problems, and deploying them to production.
• Engage with partners to advise on analytical project requirements, discuss methodologies and negotiate deliverables.
• Evaluate and deploy data analysis techniques in four key domains:
• Informative and reporting, including visualization.
• Discovery, including correlative and pattern recognition modeling techniques.
• Extrapolative, including predictive modeling techniques.
• Adaptive, including machine learning, natural language processing and adaptive modeling techniques.
• Build scalable and high-performance engines that automate the above four classes of analysis where possible.
• Rapidly test hypotheses by developing prototypes, running off-line experiments, and on-line statistical models and tests.
• Analyze pre-existing models and algorithms; suggest how to improve the efficiency and effectiveness, to drive value to the organization.
• Deliver a range of custom Data Science projects that may include Agent and Consumer Risk Modeling, Customer Lifetime Value, Customer Propensity Modeling, Image Recognition; The list goes on.
• Learn, and practice to use new tools in an inspiring technical environment that combines both coding and statistical skills, knowledge about business, customer and fraud, and real-time data.
• 3+ years of experience in a data analytics capacity.
• 3+ years of experience in technical model development and implementation, model validation, and/or model oversight.
• Thorough understanding and experience working with big data technologies.
• Advanced data wrangling skills. (e.g. high comfort level with consolidating and joining data, creating formulas, aggregating data, deduplication).
• Extensive machine learning and modeling experience and have delivered multiple projects.
• Experience with exploratory data analysis, and a conceptual understanding of time series analysis, clustering, or segmentation.
• Hands-on experience using “big data technologies”.
• Proficiency in one or more of the following: R, Python, SAS, BigQuery ML, and autoML.
• Stakeholder management; ability to independently communicate technical and statistical concepts to non-practitioners and influence the application.