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Associate Director of Machine Learning Science – Cybersecurity & Fraud
BrandBest Buy

We at Best Buy work hard every day to enrich the lives of customers through technology, whether they come to us online, visit our stores or invite us into their homes. We do this by solving technology problems and addressing key human needs across a range of areas, including entertainment, productivity, communicating with coworkers and loved ones, preparing nutritious food, providing security for your home and family, and helping you take your health to the next level.

As an Associate Director of Machine Learning Science, you’ll have the opportunity to lead a team of machine learning scientists that are researching and developing cutting-edge machine learning and artificial intelligence algorithms. In this role you will combine strategic thinking with your leadership skills, strong software engineering expertise, and deep knowledge of ML and AI algorithms to lead a team focused on architecting, developing, and operationalizing models, algorithms, and production quality codebases that enhance our core cybersecurity and fraud monitoring and prevention capabilities.

Join us if you want to spend your time:
  • Leading multiple teams focused on developing highly scalable algorithms based on state-of-the-art machine learning techniques (e.g. Transformer based language models, temporal graphs, reinforcement learning, adversarial networks, etc) that continuously monitor network event logs and transactions to identify possible cyber-attacks and fraudulent activity
  • Leveraging knowledge of software engineering principals to guide development and deployment of production machine learning solutions that can scale to millions of requests per second with millisecond latency
  • Utilizing broad and deep knowledge of machine learning and software engineering to contribute to the roadmap of Best Buy’s core machine learning capabilities
  • Coaching and mentoring machine learning scientists to elevate the talent bar across Best Buy
Required Qualifications:
  • Bachelor's degree in a highly quantitative field (e.g. Computer Science, Engineering, Physics, Math, Operations Research, etc) or equivalent experience
  • Extensive machine learning and algorithmic background with expert level understanding of at least one of the following areas: supervised and unsupervised learning methods, reinforcement learning, deep learning, Bayesian inference, graphical modeling, or nonlinear/stochastic optimization
  • 8 years of experience building ML and/or AI driven products or other similar related functions (e.g. software engineering, data science, advanced analytics). Advanced degrees in relevant fields may be counted towards experience requirements.
  • 2 years of experience leading technical research and production grade development efforts/teams
  • Fluency with at least one data science/analytics programming language (e.g. Python, R, Julia)
  • Strong software design and implementation skills with a general-purpose programming language (e.g. Scala, Swift, C++/C#, Java, Haskell, etc)

Preferred Qualifications:
  • Previous experience building ML solutions for cybersecurity and/or fraud detection
  • Master's degree or Ph.D in a highly quantitative field (e.g. Computer Science, Engineering, Physics, Math, Operations Research, etc)
  • Experience using high performance machine learning libraries and/or deep learning frameworks (e.g. PyTorch, Tensorflow, RAPIDs, etc)
  • Experience building distributed data processing pipelines (e.g. Apache Spark, Apache Beam)
  • Experience building scalable distributed products to implement batch, real-time, and streaming machine learning products
  • Strong functional programming development skills
  • Ability to effectively communicate technical information to a wide spectrum of cross-functional team members
  • Experience mentoring and coaching junior team members

Auto Req. ID792820BR
Employment CategoryFull Time
Job CategoryDigital & Information Technology
Job LevelDirector
Location Number957290-105-D&A COE-Applied Machine Learning
Address7601 Penn Avenue South


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