Sr. Data Scientist

Divisions Maintenance Group•Seattle, WA

13d•Onsite

About The Position

We are currently building out a Marketplace Health and Pricing teams and as part of it building a Data Science practice, with the goal of better leveraging advanced analytic solutions. We believe analytics will be a game-changer in the industries we operate in, and we are seeking a Data Scientist to help build and scale our data science and software engineering capabilities. At the outset, you will work on scaled and unique matching and pricing opportunities. We are a fast-paced, entrepreneurial team – this role will work across many different stakeholders to understand their needs and design solutions suitable for use across the industries we operate in. The ideal candidate will bring demonstrated experience in structured problem solving, model building and tuning, building production level analytics products, data collection & cleansing, analytics execution, and communication of meaningful insights to diverse audiences of varying seniority and familiarity with analytic concepts. We have a fully cloud native stack built primarily on AWS.

Requirements

Master’s degree in an analytical field such as Data Science, Computer Science, Applied Mathematics, Operations Research or Economics. 3 additional years of related experience may be substituted in lieu of a degree.
8+ years of relevant data science or software engineering experience developing and deploying production models and writing production code for analytics products.
Experience working with a variety of statistical and modeling techniques including hypothesis testing, supervised learning (classification and regression), forecasting, unsupervised clustering, and optimization.
Experience with Python and SQL including building python packages.
Experience gathering, interpreting, and translating business requirements into analytical solutions.
Demonstrated ability to communicate complex analytical concepts and results at multiple levels to technical and non-technical audiences.
Experience with code version control platforms like GitHub, GitLab, or Azure DevOps
Experience working with data science and analytics teams to develop complex analytics products that have been successfully delivered to customers
Experience with large-scale data wrangling using databases or Spark
Knowledge of software engineering best practices for full software development life cycle, including coding standards, code reviews, source control management, continuous deployments, and testing
Experience working with docker containers
Experience working with APIs
Experience working with a UI or web framework.
Ability to manage the stress of a fast-paced environment.
Ability to meet the in-person requirements of the team and/or business needs.

Responsibilities

Partnering with stakeholders to translate complex business problems into data science and advanced analytics solutions.
Write production level code for robust analytics products.
Collaborate with data and software engineers to support data science solutions through the entire product lifecycle, including data wrangling, exploratory analysis, hypothesis testing, modeling, rapid prototyping, business validation and testing, and deployment.
Leverage a diverse set of large and unstructured data to derive meaningful insights and information sets.
Apply a variety of advanced analytical techniques including predictive modeling, machine learning, time series analysis, simulation, and optimization.
Clearly and concisely synthesize and communicate findings to make thoughtful recommendations by combining business savvy with analytic rigor to technical and non-technical audiences.
Maintain expertise and awareness of emerging data science techniques, technologies, and potential business applications for AI/ML.