About the Team
The DCC Data Science team within Monetization Integrity (MI) is seeking bright and endlessly curious data experts to lead our data solutions practice. This group turns huge amounts of unstructured and structured data content into business insights and leverages machine learning to improve the customer experience and advertiser engagement.
About the Role
We are looking for generalists and specialists in AI/ML techniques including computer vision (CV), natural language processing (NLP), and audio signal processing. You will be responsible for partnering with a variety of stakeholders (product, operations, policy, and engineering) and developing state-of-the-art models.
What You'll Do
- Drive clarity and solve ambiguous, challenging business problems using data-driven approaches. Propose and own data analysis (including modeling, coding, analytics, and experimentation) to drive business insight and facilitate decisions.
- Develop creative solutions and build prototypes to business problems using algorithms based on machine learning, statistics, and optimization.
What You'll Need
- Knowledge of underlying mathematical fundamentals in statistics, machine learning and analytics.
- Experience with exploratory data analysis, statistical analysis and hypothesis testing, and model development.
- Fluency in SQL, Hive, Presto, or Spark and ability to write efficient code at scale with large datasets.
- Experience using Python or at least one programming language efficiently at scale with large datasets.
- Experience in building and evaluating machine learning models.
Qualifications
Basic:
- Phd, M.S. or Bachelor's degree in Statistics, Economics, Computer Science or another quantitative field. (If M.S. degree, a minimum of 1+ years of industry experience required and if Bachelor's degree, a minimum of 2+ years of industry experience required).
- Demonstrated excellence in a relevant AI/ML discipline (CV, NLP, ASR, etc.), including experience with ML model building with libraries such as TensorFlow, PyTorch, and Open AI.
- Knowledge of fundamentals of machine learning, such as algorithm families (regression, classification, unsupervised), AB testing, hypothesis testing, and optimization.
Preferred:
- Experience with human-in-the-loop ML, active learning, and data labeling.
- Experience with knowledge graphs and graph databases (e.g Neo4j, triplestores, ontologies, taxonomies).
- Strong communication skills, for example demonstrated through documentation and presentations. Able to present findings to senior management to inform business decisions.