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Full-Time

Machine Learning Engineer

PL

Confirmed live in the last 24 hours

QuinStreet

QuinStreet

501-1,000 employees

Digital marketing and online marketplace management

AI & Machine Learning
Financial Services

Compensation Overview

$120k - $160kAnnually

+ Performance Bonus + Commission + Equity Grants

Junior

Remote in USA

Category
Applied Machine Learning
AI & Machine Learning
Required Skills
Python
Requirements
  • 2+ years’ experience in software development
  • MS in Computer Science or related area, or relevant work experience
  • Deep experience in applied machine learning, statistical learning, mathematical optimization and scalable computing
  • Experience with both supervised and unsupervised learning on large datasets
  • Succeed as an individual but to collaborate and strive for the success of your team
  • Strong sense of ownership in everything you build
  • Strong experience building production level code (Python is a plus)
Responsibilities
  • Use your expertise in machine learning and statistical learning techniques such as classification and regression to produce models applicable to pricing and targeting.
  • Apply mathematical optimization techniques to solve network problems.
  • Experiment and build models that improve the capabilities of our marketing platform and allow for increases in yield.
  • Work with our Engineering and Business teams to implement scalable, reliable and useful solutions for our various business units.
  • Run numerous experiments in a fast-paced, analytical culture so you can quickly learn and adapt to your work.
  • Get the satisfaction of knowing that you helped connect millions of visitors to our affiliates and internal properties to the products and services they are searching for.

QuinStreet creates and manages online marketplaces that connect consumers with brands offering specific products or services. Operating in sectors like insurance, personal loans, credit cards, banking, and home services, QuinStreet uses advanced segmentation and AI technologies to efficiently match consumers with the right brands. The company follows a performance-based business model, where brands pay only for measurable results such as leads or clicks, outlined in detailed agreements called Insertion Orders. This model allows clients, primarily in financial and home services, to receive highly targeted customer prospects, leading to improved marketing outcomes and higher ROI. QuinStreet's expertise in digital media positions it as a valuable partner for brands aiming to enhance their online presence and customer acquisition efforts.

Company Stage

IPO

Total Funding

$38.9M

Headquarters

Foster City, California

Founded

N/A

Growth & Insights
Headcount

6 month growth

2%

1 year growth

1%

2 year growth

11%
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Simplify's Take

What believers are saying

  • Significant investments from firms like Harbor Capital Advisors and Morgan Stanley indicate strong market confidence in QuinStreet's business model.
  • The company's focus on performance-based marketing ensures that clients receive high value for their marketing spend, potentially leading to long-term partnerships.
  • QuinStreet's expertise in digital media and targeted marketing can drive substantial improvements in clients' online presence and customer acquisition strategies.

What critics are saying

  • The performance-based model, while attractive, may lead to revenue volatility depending on campaign success.
  • The competitive landscape in digital marketing is intense, requiring continuous innovation to maintain a leading position.

What makes QuinStreet unique

  • QuinStreet's performance-based model ensures clients only pay for measurable results, providing a clear ROI advantage over traditional marketing methods.
  • The company's use of advanced AI and segmentation technologies allows for highly targeted customer acquisition, setting it apart from competitors.
  • QuinStreet's strategic acquisitions of high-value domains like Insure.com and Insurance.com bolster its authority and presence in the insurance sector.