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Nicholas Kridler

Director, Machine Learning at Goodwater Capital

San Diego, California

Overview 

Nicholas Kridler is the Director of Machine Learning at Goodwater Capital in San Diego, California. With a background in data science and machine learning, he has held key roles at companies like Dia & Co, Faire, and Intuit, showcasing expertise in Python, algorithms, data analysis, and computer vision. Kridler's career highlights include leading machine learning teams, contributing to the growth of startups through data-driven strategies, and leveraging his expertise in scientific computing and numerical analysis to drive impactful results in the AI sector.

Work Experience 

  • Director, Machine Learning

    2022 - Current

    Lead a team of data scientists to identify promising startups - Develop KPI models to surface interesting companies - Improved operationalization of ML strategies

Goodwater Genesis seeks out exceptional entrepreneurs and helps them cultivate the success they deserve.

  • Lead Machine Learning Engineer

    2021 - 2022

    Real-time ML-based Product Recommendation Algorithms - Maintained infrastructure for recommendation systems: real-time ranking models deployed as microservices - Developed and deployed an embedding based personalization module for the e-commerce shop - Provided cost savings via improvements to our dynamodb cache Analytics Platform Support - Developed custom ETLs that retrieved data from ad platforms via REST APIs (e.g. Pinterest) - Developed ETLs for additional data sources in dbt - Provided Looker support to business stakeholders

Dia&Co sells plus size clothing and accessories via a try at home model.

Raised $90,000,000.00 from TriplePoint Venture Growth, Union Square Ventures and Sequoia Capital.

  • Senior Data Scientist

    2020 - 2021

    Led apparel ranking initiatives on the search and personalization team - Delivered a 10% increase to apparel orders through changes to the search algorithms - Developed a process to improve roadmap alignment between the product and ranking teams - Plan Quarterly goals (OKRs) and identify the potential lift in revenue associated with each project Contributed to the core search ranking models - Introduced new features to help characterize predictive behavior of users in search - Train ranking models in interactive (Sagemaker) and batch (Batch) environments - Improve search relevance by designing retrieval algorithms in elasticsearch End-to-End ownership of ranking algorithms - Responsible for backend production algorithm implementation in Kotlin - Develop offline frameworks in Python to evaluate algorithm improvements - Responsible for analyzing A/B tests using variance reduction techniques (e.g. CUPED) to assess the overall impact

Faire is a marketplace and wholesale platform that helps retailers find and buy wholesale merchandise for their stores.

Raised $1,708,162,662.00 from Shopify.

  • Senior Data Scientist

    2020 - 2020

    Drove adoption of Contextual Bandits - Leveraged ML platform to accelerate the adoption of contextual bandits - Presented proof-of-concept to Senior Leadership Team Influenced product strategy through the development of new metrics - Developed metrics that help characterize customer engagement with Turbo Tax Pioneered new technologies for the team - Led effort to deploy models to the ML platform - Developed tools to enable data scientists and analysts automate their workflows

Intuit Turbo is an app to get financial reports and financial advice.

  • Lead Machine Learning Engineer

    2018 - 2019

    Developed product recommendations for the styling service - Leveraged binary classification models for high-dimensionality categorical data - Deployed models through RESTful API microservices Increased sales through recommender model enhancements - Improved recommendations for new customers by tuning the model and training data - Algorithmically combined recommendations to improve stylist efficiency - Evaluated model enhancements through AB tests End-to-End Data Products to support the recommendation platform - Developed dashboards to monitor algorithm performance - Developed metrics to evaluate inventory health Hands-on project management - Weekly one-on-one meetings with team members - Develop roadmap and manage projects via Agile/Scrum

Dia&Co sells plus size clothing and accessories via a try at home model.

Raised $90,000,000.00 from TriplePoint Venture Growth, Union Square Ventures and Sequoia Capital.

  • Data Scientist

    2014 - 2018

    End-to-End ML-based Data Products - Developed a data source used to create ML features that powered multiple capabilities - Built and maintained ETLs using Spark (SQL and PySpark) - Created binary classification models to identify product expansion opportunities - Developed simple web applications to surface recommendations to business partners Drove Vendor Partnership through Sharing Data - Introduced a vendor-facing data portal for sharing performance metrics - Used NLP techniques to characterize customer feedback for each product - Introduced a vendor-buyer collaboration tool for capturing data needed to power other data science initiatives Hands-on project management - Responsible for project scope, implementation, and delivery - New-hire onboarding and mentorship

  • Senior Data Scientist

    2014 - 2014

    Transitioned legacy AB Testing Framework code to Python - ETLs and statistical tests to track the results of in-game experiments - Constructed an API to assess potential test biases due to player cohort imbalances Worked towards developing a centralized datastore across all studios - Responsible for defining data model and extracts for the Redshift data warehouse - Collaborated with the engineering team to design aggregate tables Identified the scale and missed revenue opportunity caused by in-game cheating - Created an anomaly detection algorithm to look for abnormal game actions - Presented findings to stakeholders to address revenue loss

  • Senior Data Scientist

    2012 - 2014

    Leveraged predictive modeling techniques to improve internal processes - Developed a prioritization algorithm to optimize work-flow Identified potential opportunities for missed revenue using Matrix Factorization - Developed a prototype One-Class Collaborative Filter in R - Successfully identified opportunities not found by human auditors Predicted Inpatient length-of-stay using historical Medicare data - Developed a physician aid for assessing the need for inpatient admittance - Utilized a decade of Medicare claims data

R1 RCM serves as a revenue cycle management partner for hospitals and healthcare systems regardless of the payment models.

Raised $200,000,000.00 from CD&R LLP.

  • Staff Scientist

    2006 - 2012

    Apply machine learning techniques to reduce false alarms in detection systems. -Utilize decision trees and support vector machines to discriminate false alarms -Develop additional derived metrics to boost performance Produce end-to-end solutions from data ingestion to actionable insights. -Generate scripts to scrub data and extract additional features -Present results at quarterly meetings Develop production software leveraging GPU image processing in OpenGL. -Implement image filters as GLSL shaders Develop research software for automatic target recognition in OpenCV. -Design C++ software for wide-area template matching

  • Analyst

    2005 - 2006

    Conduct Operations Analysis studies using the Naval Simulation System (NSS). Interface with customers to develop and implement new features in NSS. Instruct training classes for new NSS users.

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