Affectiva is an MIT Media Lab spin-off and the leading provider of AI software that analyzes facial and vocal expressions to identify complex cognitive and emotion states.
Our patented AI software uses machine learning, deep learning, computer vision and speech science. Affectiva has built the world’s largest emotion data repository with over 6M faces analyzed in 87 countries. Affectiva is used by one third of the Fortune Global 100 for content testing and is now working with leading automotive OEMs and Tier 1s on next generation driver state monitoring and in-cabin mood sensing.
As you can imagine, such an ambitious vision takes a great team with a strong desire to explore and innovate. We are growing our team to improve and expand our core technologies and build products that disrupt billion dollar industries such as advertising and automotive.
Position
As the leaders in Emotion AI, data is critical to maintaining Affectiva’s market leadership and protecting our “first mover advantage”. In this position, you would be responsible for defining our data acquisition and annotation strategy and executing on that strategy. We have already amassed the world’s largest emotion data repository and are accelerating our plans to collect (through partners as well as our own data acquisition strategies) and annotate data, especially in new markets. You are perfect for this role if you have a background in data science and combine that with a creative and visionary mind to own our data strategy and execute on it.
The Director of Data reports directly to the CEO.
Responsibilities:
- Ownership of Affectiva’s data acquisition and annotation strategy both near & long-term
- Execution of data acquisition plan to aggressively acquire data to meet Affectiva’s research and product needs
- Development and implementation of a data partnership strategy including academic and commercial partner outreach and developing a data ecosystem or consortium
- Oversee Affectiva’s data warehouse and pipeline – from acquisition to annotation
- Partner with our science and engineering teams to improve our data infrastructure and processes
Qualifications:
- 5 years experience working at a technology company in data warehousing, data science, data mining, and/or machine learning
3 years experience working with web-based data infrastructure:
SQL, NoSQL, S3,Hadoop/EMR, AWS Batch, Docker, Parquet, Hive, Presto - Advanced degree in data science/machine learning or equivalent industry experience leveraging machine learning and/or data science
- Creative view towards data gathering methods – Guerrilla DIY, defining existing data sets that can be accessed or acquired, engaging with strategic partners who have or can get data, etc.
- Ability to collaborate with key stakeholders in product management, R&D and executive team, and represent needs of varying teams
- Excellent presentation and communication skills
- Desire and ability to build a data team