William Gagne-Maynard

Senior Data Scientist at Shelf Engine
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Contact Information
us****@****om
(386) 825-5501
Location
Greater Seattle Area, US
Languages
  • French Limited working proficiency
  • Portuguese Limited working proficiency

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Bio

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Experience

    • United States
    • Software Development
    • 1 - 100 Employee
    • Senior Data Scientist
      • Mar 2022 - Present

      * Developed models of current store inventory to feed into a grocery order optimization engine in Python.* Built and tested censored demand model to correct for stockouts in our sales data, leading to decrease in model bias.* Built systems for incorporating exception events into our demand forecasts and smoothing orders based on business impact.* Created waste attribution framework to track business critical metrics and link to individual components of our models for investigation and improvement with cross-functional stakeholders.

    • United States
    • Transportation, Logistics, Supply Chain and Storage
    • 500 - 600 Employee
    • Senior Data Scientist
      • Sep 2020 - Feb 2022

      Senior Data Scientist working on the team responsible for providing products to allow us to price and manage long-term freight contracts.* Worked cross-functionally across product, engineering and science to design, implement and analyze/size improvements to our bid optimization product.* Developed much of our existing cost forecasting infrastructure for predicting future freight rates.* Worked with downstream pricing operations team and business stakeholders to analyze impact of model changes and proposed product changes on financial metrics.* Contributed to production codebase in Python, adding shipper-level estimates of variable cost, improving our cost forecasts and automating model evaluations and backtests.* Conducted statistical analysis of experiments and model changes.

    • Data Scientist, Marketplace Demand
      • Feb 2019 - Sep 2020

    • United States
    • Software Development
    • 1 - 100 Employee
    • Data Specialist
      • Mar 2018 - Nov 2018

      At TINYpulse I worked across our Product, Marketing and Customer Success teams to help drive analytical insights in a number of capacities. I was responsible for defining and tracking KPIs and other metrics across departments. I pulled and combined data from a variety of our service's APIs, Google Analytics, and our internal databases to automate the generation of reports and dashboards using MySQL, Python, RShiny and Tableau. I also partnered with product owners to measure the effect of user tests and experiments on product usage.I was also responsible for diving into our product usage data and identifying areas of improvement. I've developed predictive models for estimating company health and customer churn and used this data to inform our Customer Success team's outreach methods. I have also worked to develop machine learning models to identify high-performing employees and attrition risks among our customer's data.

    • United States
    • Research
    • 300 - 400 Employee
    • Data Analyst
      • Mar 2017 - Mar 2018

      Worked as a cross-cutting analyst with the Geospatial Team helping several teams of researchers model a range of geospatial health indicators on a 5x5km scale. I developed ETL pipelines using Python and R to load, clean and merge large datasets for input into our models. I was also responsible for creating data visualization tools and dashboards using R Shiny for evaluating both our model inputs and results and detecting anomalies. Worked as a cross-cutting analyst with the Geospatial Team helping several teams of researchers model a range of geospatial health indicators on a 5x5km scale. I developed ETL pipelines using Python and R to load, clean and merge large datasets for input into our models. I was also responsible for creating data visualization tools and dashboards using R Shiny for evaluating both our model inputs and results and detecting anomalies.

    • United States
    • Higher Education
    • 700 & Above Employee
    • Research Assistant
      • Sep 2013 - Sep 2016

      At UW, I worked with a team of researchers in the department of Oceanography on a project dedicated to studying Carbon fluxes from large rivers. As a part of this team, I was responsible for cleaning, filtering and merging data from multiple environmental data sets using Python and SQL. I developed statistical models to help predict river fluxes using Python and R statistical packages. I generated maps and data visualizations for public dissemination as well as articles for scientific publication.

Education

  • University of Washington
    Master’s Degree, Chemical Oceanography
    2013 - 2016
  • Carleton College
    B.A., Chemistry
    2009 - 2013

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