About the position
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 for various lines-of-business, inclusive of Consumer, Film, Streaming, and Ad Sales.
- Establish best practices for engineering, monitoring, operationalizing, and improving automated ML models and APIs.
- Lead the prototype, architecture, and selection of ML and Data Science platforms.
- Partner closely with business, product owners, engineering, and data science teams to implement design patterns that optimize performance, cost, security, and scale.
- Support advanced analytics efforts and deep-dive analysis to answer specific business questions.
- Mentor and guide engineering and data science peers in building a comprehensive set of tools, knowledge, and standards.
Requirements
- Strong understanding of applied statistics and machine learning algorithms.
- 8+ years of data science experience, with demonstrated proficiency using SQL and Python.
- 3+ years of experience building production-deployed, well-monitored MLOps solutions.
- Experience in AutoML, NLP, and Deep Learning frameworks.
- Intimate familiarity with ML architectures and workflows on the cloud (AWS, GCP, Azure).
- Hands-on experience with Snowflake, Databricks, Spark, Airflow, or related cloud-based big-data storage and orchestration technologies.
- Bachelor's/Master's degree in Computer Science, Data/Decision Science, Data Engineering, Quantitative Economics, or Mathematics.
Nice-to-haves
- Direct experience working with media/entertainment and ad sales datasets and platforms.
- Ability to report and demonstrate model-development progress to stakeholders.
- Strong understanding of Agile principles and best practices.
- Comfortable with ambiguity and making decisions in a dynamic, fast-paced environment.
- Excellent verbal and written skills with the ability to communicate data stories effectively.
Benefits
- Medical, dental and vision insurance
- 401(k)
- Paid leave
- Tuition reimbursement
- Variety of other discounts and perks
