Machine Learning Engineer – University Hire

Job description

DESCRIPTION

The PeopleInsight team is looking for a Machine Learning (ML) Engineer to help our ML group develop and deploy machine learning solutions for various teams within Worldwide Operations. The ML team builds scalable end-to-end impressive production machine learning solutions for World Wide Operations. Our solutions range from predicting the timing of critical events to running simulations that are used for long term strategic planning in World Wide Operations. ML models are typically deployed as components of larger software projects so we work collaboratively with teams consisting of product managers, UX designers, data engineers and operations leaders. We build and deploy models following high software standards emphasizing speed and scale leveraging world class AWS infrastructure. The team constantly challenges assumptions and works to clarify how models perform, and how they impact the user experience. The ML team invests heavily in R&D, as the field evolves, we will remain in touch with the state-of-the-art. This is an exciting area of Amazon to gain real world ML experience and insight!

We are now recruiting an exceptional Machine Learning Engineer for Worldwide Operations.

Success in this role will require:
• Patiently conducting error analysis on a baseline model, and deliberate and clear-eyed tuning to improve model performance. The ML engineer should consider constraints in the data pipeline and UI phases when evaluating models.
• The ML engineer will write efficient and prudent programs in Python and demonstrate attention to detail when debugging. A successful candidate will anticipate software bugs as well and write unit tests to prevent them.
• ML engineers will help develop the data acquisition pipelines and data labeling workflows with embedded SQL, pySpark, or other Python based tools.
• Good intuitions as to what heuristics or data transformations can be applied to data that are motivated by domain expertise.

The ideal candidate will:
• Have a T-shaped skill set; a strong foundation in ML/AI, computer science, software engineering, statistics, mathematics or related field. Along with a single area of specialization such as NLP, recommendation engines, time-series forecasting, computer vision, etc.
• Building solutions that not merely work, but solutions that permit the fastest deployment, at scale.
• Through error analysis, identify whether new data sources are required or more observations are necessary
• Have experience deploying an ML model through a web or app-based API.
• Have experience creating data labeling workflows with cloud-based tools, ideally AWS.
PeopleInsight is a collaborative group of product, program, design, science, and engineering experts.

Our Worldwide Operations team numbers in the hundreds of thousands of dedicated associates and leaders. They deliver millions of packages to customers every day. PeopleInsight is the behind-the-scenes team that supports field and organizational leadership by advising action, informing decisions, and identifying risk. The PeopleInsight team resides at the intersection of words and numbers. We listen, observe, and measure at scale to support customer success with insights that are consumable, actionable, and relevant. Our work is dedicated to empowering leaders and enabling action through data and science. In addition to standard reporting, we leverage analytics to help our leaders focus their efforts in ways that will engage, retain and grow their associates to propel the business forward.

Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation / Age

Desired profile

BASIC QUALIFICATIONS

• Bachelors of Degree in Computer Science, Data Science, Statistics, or related field
• 1+ years of experience in machine learning, statistical modeling or software development. Graduate work or significant course implementations are considered as experience.
• Solid foundation and understanding of Machine Learning and Data Science fundamentals.
• Proficiency in, at least, one modern high-level programming language such as Python, R, Java
• Ability to develop data acquisition pipelines and data labeling workflows with embedded SQL, pySpark, or other Python based tools.
• UNIX, shell scripting