Job Location: Boston, MA
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 Machine Learning Scientist within the Applied Machine Learning – Personalization team, you will have the opportunity to work alongside industry experts researching, developing, and applying cutting edge experimentation methodologies. Your contributions to the existing experimentation platform will allow large portions of Best Buy’s internal product and business teams to rigorously test their hypothesis on what improves customer experience for 100s of millions of Best Buy customers.
In this role you will combine your knowledge of ML and AI algorithms with strong research expertise to develop experimentation models that help product stakeholders across Best Buy to conduct online and offline experimentation. You will work alongside a passionate group of researchers within AML and also, engineers and product managers to understand the internal client problem, formulate the right kind of experimentation methodology, run the tests, analyze the results using a combination of statistics & ML and finally deliver data-driven insights on next steps to the business stakeholders.
Join us if you:
- Are passionate about developing experimentation methods to improve business decisions
- Are keen on reading the latest research in Reinforcement Learning, multi-arm bandit, causal inference (e.g., DoWhy) and implementing them in Python
- Have prior experience with offline & online policy evaluation techniques and/or Bayesian statistics ideally at a large retail organization
- Are comfortable working with the technical and non-technical stakeholders to educate team on relevant evaluation methodologies and their risks/benefits
- Have experience in modifying libraries to develop custom models and metrics to quantify uplift
What will you do?
- Interacting with product and business stakeholders to translate business problems into technical formulations (such as causal graphs, etc.)
- Evaluating existing open-source packages to see if they meet the requirements for internal use
- Sourcing data from different tables and plugging them into the models
- Working with the platform teams to productionize and debug experimentation methods
- Clarifying to the stakeholders why certain modelling approaches were taken and clearing misconceptions about statistical concepts (p-value, Simpson’s paradox, confounders, etc.)
- Analyzing experimentation results to identify and suggest the best policy to implement
Minimum Requirements:
- Bachelor’s degree in a highly quantitative field (Computer Science, Engineering, Physics, Math, Operations Research or related) or equivalent experience
- 2 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.
- 1 year of background in at least one of the following areas: reinforcement learning, Bayesian inference, graphical modeling, multi-arm bandit or causal inference
- 2 years of programming with at least one data science/analytics programming language (e.g., Python, R, Julia)
Preferred Qualifications:
- Master’s degree or Ph.D in a highly quantitative field (Computer Science, Engineering, Physics, Math, Operations Research or related)
- Experience building production grade ML models using one or more Deep Learning frameworks like TensorFlow, Keras, PyTorch, etc.
- SQL experience
- Previous experience in retail
- Building & deploying ML products over GCP and/or AWS
- Strong functional programming development skills
- Ability to effectively communicate technical information to a wide spectrum of cross-functional team members and to leadership
- Open-source contributions or robust GitHub portfolio
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