Dave Lin

Dave Lin

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Head of Product and Engineering
San Diego, California, United States

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Résumé

Jobs verified_user 0% verified
  • Counterpart
    Head of Product and Engineering
    Counterpart
    Jun 2023 - Current (3 years 3 months)
  • Amazon
    Principal Technical Product Manager - New Business Growth - Alexa
    Amazon
    Oct 2020 - May 2021 (8 months)
    Created product pitch and resourcing alignment, obtained Alexa COO's approval for MVP to enable third-party developers and solution integrators to build and deploy experiences on Alexa-enabled devices in healthcare industry.
  • Hookit A KORE Company
    VP of Engineering
    Hookit A KORE Company
    Oct 2019 - Oct 2020 (1 year 1 month)
    * Formalized software development life cycle and streamlined its development and operations resource, resulting in a 40% reduction in operation and infrastructure spend. * Led the rebuild of the company's proprietary social media valuation models in Snowflake, showcased in a Snowflake case study.
  • Hookit A KORE Company
    Chief Technology Officer
    Hookit A KORE Company
    Nov 2017 - Mar 2023 (5 years 5 months)
    Key driver in transforming Hookit into a platform with 100+ global brand customers from a 20-person startup. Company was acquired in 2022. Built its product development processes, scaled its data pipelines, streamlined its technology resources, implemented necessary security controls, and led the technical due diligence process during its successful exit (acquired by Kore Software on 02/2022). 02/2022 - 03/2023: CTO - Hookit (Post-acquisition) * Led post-acquisition integration of Hookit into Kore Software, including migrating from a colocated data center to AWS with no application downtime (20% cost reduction). * Developed a generative AI solution to optimize sponsorship selling cycle, extracting brand insights and values, crafting target
  • Hookit A KORE Company
    Director of Product Management
    Hookit A KORE Company
    Nov 2017 - Oct 2019 (2 years)
    *Influenced company's pivot to serving large brands, such as Red Bull, Vans, Taylor Made * Defined and launched the MVP brand offerings, including prototyping a model to predict sponsorships from social media data. * Built and grew product function, consisting of product, design, and data science.
  • Apple
    Senior Technical Product Manager
    Apple
    Feb 2016 - Nov 2017 (1 year 10 months)
    * Led creation and launch of Apple's first iOS Search Ads platform within 8 months. Defined account and user ACL data models, campaign management states, reporting API and UI, billing logic and system integrations, and internal Salesforce CRM data models and workflows. Received departmental award for the effort. * Defined end-to-end architecture and integrations with internal finance and tax systems to enable global expansions to EMEA and APAC countries. * Pitched, defined, and led the execution of a new pay-per-installed based Search Ads Basic.
  • Yext
    Senior Product Manager
    Yext
    Aug 2012 - Feb 2016 (3 years 7 months)
    Founding member of the product management team, responsible for defining and executing product roadmap during the company's growth from a 60-person startup to an IP-ready organization. Notable accomplishments include: * Introducing enterprise analytics capabilities that supports 50+ external and internal data sources. * Internationalizing core local listings delivery system, enabling enterprises to easily manage public information across 100+ global data providers, including Apple Map, Facebook, and Yelp. * Designing a system that allowed marketers to target in-store visitors with personalized ads on Facebook, Twitter, and Yahoo, using data collected by a network of mobile apps listening for in-store beacon signals.
  • Intermedia Cloud Communications
    Product Manager
    Intermedia Cloud Communications
    Jan 2011 - Aug 2012 (1 year 8 months)
    Managed product strategies and roadmap of reselling partner channels, consisting of over 10,000 reselling partners and four distribution channels.
Education verified_user 0% verified
  • UC San Diego
    B.S, Electrical Engineering
    UC San Diego
  • UC San Diego
    M.S, Electrical Engineering
    UC San Diego
    First author of two IEEE conference papers
Projects (professional or personal) verified_user 0% verified
  • G
    GPT Lab
    GPT Lab is a Streamlit app that lets users create, share, or chat with their own LLM prompt-based AI chatbots without worrying about the underlying infrastructure. The app is featured on Streamlit's LLM app gallery. In 3 months post launch, the app garnered over 9K+ app views, 1200+ unique signed-in users, and 200+ user-created assistants. It was featured on two different Streamlit blogs and a Snowflake user group. The Github repo has received 100+ stars and 100+ forks, and the app is featured in the Streamlit's LLM app gallery.
Publications verified_user 0% verified
  • I
    Human Vision System Aware Exhaustive Block-matching Algorithm
    IEEE ICME Jul
    In this work, homomorphic image modeling is used to make the exhaustive block-matching algorithm (EBMA) more human vision system (HVS) aware, thus yielding visually pleasing sequences. Homomorphic signal processing is used to separate the luminous and the structural components of each frame; EBMA is then applied to both components to capture the luminous and the structural changes. The combination of these two techniques is utilized to simulate the structure preserving nature of HVS. Temporal irrelevancies are reduced by removing the excess motion fields from both components. The homomorphic EBMA (H-EBMA) processed sequences have superior visual quality despite mean PSNR values that are comparable to sequences produced by traditional EBMA.
  • I
    Objective Human Visual System Based Video Quality Assessment Metric for Low Bit-Rate Video Communication Systems
    IEEE Multimedia Signal Processing Oct
    Quality assessment is crucial to developments of video communication systems. While subjective test yields most accurate results, its expensive cost and human biasness make it an unsatisfactory figure of merit. The peak-signal to noise ratio (PSNR), the most acceptable objective video quality assessment metric, is based on error sensitivity; its failure to capture perceptual errors is well documented. Other perceptual quality metrics fail to detect the localized errors that are common in sequences produced by low bit-rate video communication systems. In this work, a novel perceptual quality assessment metric, the structural similarity (SSIM) metric, is modified to better detect spatial artifacts often seen in video communication systems
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