Machine Learning Engineer

Company Description

We create world-class content, which we distribute across our portfolio of film, television, and streaming, and bring to life through our theme parks and consumer experiences. We own and operate leading entertainment and news brands, including NBC, NBC News, MSNBC, CNBC, NBC Sports, Telemundo, NBC Local Stations, Bravo, USA Network, and Peacock, our premium ad-supported streaming service. We produce and distribute premier filmed entertainment and programming through Universal Filmed Entertainment Group and Universal Studio Group, and have world-renowned theme parks and attractions through Universal Destinations & Experiences. NBCUniversal is a subsidiary of Comcast Corporation.

Here you can be your authentic self. As a company uniquely positioned to educate, entertain and empower through our platforms, Comcast NBCUniversal stands for including everyone. Our Diversity, Equity and Inclusion initiatives, coupled with our Corporate Social Responsibility work, is informed by our employees, audiences, park guests and the communities in which we live. We strive to foster a diverse, equitable and inclusive culture where our employees feel supported, embraced and heard. Together, we’ll continue to create and deliver content that reflects the current and ever-changing face of the world.

Job Description

As part of the global Operations & Technology organization, the Data & Analytics group (D&A) is focused on the data strategies for the future, covering the entire analytics life cycle – data engineering, data architecture, data platforms, data analysis, data viz, and data science. We support NBCU’s vast portfolio of brands – from broadcast, cable, news, and sports networks to film studios, world-renowned theme parks, and a diverse suite of digital properties. We take pride in providing NBCUniversal with data to advise and shape strategic business decisions. 

The Data & Analytics team is looking for a passionate Machine Learning Engineer adept at building the next generation of analytics solutions and ML pipelines. The candidate will be working closely with internal stakeholders, data engineers, visualization experts, and other technologists across one or more of our main subject areas. 

This role is right for you if you are a subject matter expert in designing end-to-end data science solutions and can maintain the fine balance of business acumen and deep technical knowledge. You are a passionate problem solver who is looking to build the next generation of products and applications for ML models. You are also a hands-on coder and architect who can create scalable (even self-healing) machine-learning pipelines in the cloud. 

Responsibilities: 

• Drive end-to-end MLOps development to create scalable production ML pipelines (e.g., feature creation, training, and inference) for various lines-of-business, inclusive of Consumer, Film, Streaming, and Ad Sales. These automated products will aid in the timely decision-making process for marketing, strategy, and targeted advertising 
• Establish best practices for engineering, monitoring, operationalizing, and improving automated ML models and APIs, with consideration for the levels of user expertise, cloud ecosystem capabilities, and transferrable standards 
• Lead the prototype, architecture, and selection of ML and Data Science platforms, cognizant of the varied levels of user expertise, technical capabilities, and trends 
• Partner closely with business, product owners, engineering, and data science teams to implement design patterns that optimize performance, cost, security, and scale, and that complement company-wide architectural standards 
• Support advanced analytics efforts and deep-dive analysis to answer specific business questions, and develop augmented-analysis tools to enable efficient business decisions 
• Mentor and guide engineering and data science peers in building a comprehensive set of tools, knowledge, and standards that can be democratized across the organization 

Qualifications

• Strong understanding of applied statistics and machine learning algorithms, experience in methodologies for regression, classification, clustering, and causality 
• 8+ years of data science experience, with demonstrated proficiency using SQL and Python in building analytical solutions 
• 3+ years of experience building production-deployed, well-monitored MLOps solutions to solve business problems, with particular preference for the media industry 
• Experience in AutoML, NLP, and Deep Learning frameworks that enrich MLOps efficacy 
• Intimate familiarity with ML architectures (e.g., MLflow) and workflows on the cloud (i.e., AWS, GCP, Azure) 
• Hands-on experience with S