Analytics Product Owner

Role Overview

• Sets the strategy and tone for the data science strategy and vision for the future.
• Prioritizes analytical projects based on business value and technological readiness.
• Owns the entire model development process, from identifying the business requirements, data sourcing, model fitting, presenting results, and production scoring.
• Provides leadership, coaching, and mentoring to team members and develops the team to work with all areas of the organization.
• Identifies the roles, skills, and knowledge required to achieve organizational goals, and forecasts new skill requirements.
• Leads the evaluation of tools with key stakeholders across varying management levels to prioritize analytics use cases and requests and maintain the overall book of work.
• Plans and ensures the execution of work, and achievement of team goals.
• Works with stakeholders to ensure that business needs are clearly understood and that services meet those needs.
• Anticipates and analyzes trends in technology while assessing the emerging technology’s impact(s).
• Coaches' individuals through change and serves as a role model.
• Performs other duties as assigned.

Requirements

• Master’s degree in computer science, statistics, economics or related fields
• 10+ years of experience in strategy, management consulting, or similar skillset, experience working with analytics, and technical leadership and management experience on data-centric products
• Hands-on manager with a proven track record of leading and growing successful data teams
• PhD in computer science, statistics, economics or related fields (preferred)
• Ability to foster an inclusive and innovative working environment
• Demonstrated negotiation, influencing, partnership & presentation/communication skills
• Ability to understand business challenges and translate them into value added, data driven solutions
• Strong knowledge in predictive modeling methodology
• Experienced at leveraging both structured and unstructured data sources
• Willingness and ability to learn new technologies on the job
• Demonstrated ability to communicate complex results to technical and non-technical audiences
• Experience conducting statistical analysis
• Experience with big data analysis tools and techniques
• Experience building and deploying predictive models, web scraping, and scalable data pipelines
• Demonstrated Expertise with at least one Data Science environment (R/RStudio, Python, SAS) and at least one database architecture (SQL, NoSQL)
• Strong experience with Cloud Machine Learning technologies (e.g., AWS Sagemaker)
• Strong experience with machine learning environments (e.g., TensorFlow, scikit-learn, caret)
• Expert understanding of statistical methods and skills such as Bayesian Networks Inference, linear and non-linear regression, hierarchical, mixed models/multi-level modeling
• Financial Services background.

Tech Stack

• AWS
• Cloud
• NoSQL
• Python
• Scikit-Learn
• SQL
• Tensorflow

Benefits

• Health insurance
• 401(k) matching
• Flexible work arrangements
• Professional development opportunities