Machine Learning Engineer

August 15

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Description

• You’ll help our data science team grow by sharing your ideas, challenging status quo, and influencing the direction of the team • You’ll demonstrate a broad knowledge of ML approaches and you are pragmatic about solutions. E.g., you create business value and fit models • You’ll work across supervised, unsupervised, and deep learning approaches while being comfortable using inferential statistics, time series, LLMs, or computer vision approaches to solve a given problem (we don’t expect expertise in all!) • You’ll design, engineer, test, document, and explain production ML systems in Azure, Python, SQL, and Git • You’ll take the lead gathering input from non-technical customer stakeholders and senior IT leadership to plan what to build and set project scope • You’ll frame complex problems and propose options to either use data when it’s available or pursue alternatives when it’s not • You’ll take the lead in technical design discussions, remove blockers, and align stakeholders • You’ll use Big Data tools (Databricks, Spark, Azure, SQL, Python) to carry out supporting analyses and build production solutions • You’ll submit your daily hours billed to cloud-based project management software, Kantata, for time tracking • You’ll fit, tune, test, validate, and deploy ML models in an MLOps setting to solve real-world problems • You’ll discuss project issues and topics simply when presenting to customers

Requirements

• Strong coding skill in SQL and Python (especially pandas, numpy, scikit-learn) • Over 5 years of experience in the applied Machine Learning and Data Science field. And at least 3 years of experience owning ML systems from idea to maintenance • Relative experience (~10+ projects) in cloud development. Azure is preferred but AWS, GCP, or other relative cloud platform experience is acceptable • Professional experience using Databricks and Spark • Demonstrable thought leadership evidenced by an active GitHub, blog posts, or publication record • Deeply curious about the world • Willing to challenge the status quo to propose improvements • Motivated by challenge and do not shy away from potential failure • Can communicate simply and intuitively to diverse audiences with varying technical background • Proven success with--and interest in--owning end-to-end projects solo but knowing when to escalate and delegate • Experience with--and interest in—developing in a production MLOps setting • Practical experience with tools for the ML lifecycle, including EDA, data prep, data verification, modeling, testing, deploying, and relevant object-oriented software development tasks • Experience using data visualization to understand and persuade