Director, Team Manager
Remote - USA
About this role:
The role will be responsible for leading a Data Science team that creates and operationalizes Machine Learning models and automated tools that use internal and external data to create benchmarking metrics and differentiated insights for research publications and client advisory services.
What you’ll do:
- Create a strategy and overall vision for where and how data science can further development of scaled insights for our clients’ most critical needs. A few potential projects or domains you may get involved with:
- Applying Natural Language Processing (NLP) to classify content created by/and-or about brands
- Delivering dashboards (Tableau) and web apps (Streamlit) for self-service insights for domains like Social Media and Search Marketing
- Creating contextual word embedding models/packages to accelerate development of existing and new tools
- Ingesting and delivering insights from new external data assets in collaboration with Product and Engineering teams
- Manage Data Science team operations and talent development
- Identify new data sources that could add value to our research practice
- Work with data engineers and product managers to develop analytics and ML pipelines to support scalable analysis and internal tools
- Partner directly with peer managers in Product and Survey Analytics to align user & client needs with the Data Science team
- Partner closely with Global Product Management (Customer & Market Research) to deliver insights to the practice and support product development, research agendas, and sales enablement
What you’ll need:
- Bachelors in a quantitative research field (e.g., Computer Science, Electrical Engineering, Applied Mathematics, Physics, etc.).
- 10 + years of total experience, including 3-5 years progressive hands-on experience building predictive models, recommendation systems, and/or NLP/text mining tools.
- 2+ years of experience managing a team of junior data scientists
- Experience managing a budget
- Well-versed in the foundational approaches to the major data science disciplines, such as data preparation, advanced statistics, machine learning, data pipelining and natural language processing
- Practical, intuitive problem solver with a demonstrated ability to translate business objectives into actionable data science tasks and translate quantitative analysis into actionable business strategies
- Connections to the external ML/Data Science community
- Experience and proficiency with various programming languages (Python), machine learning models (transformers), statistical packages, SQL/relational databases, and cloud computing (AWS, GCP)
- Experienced in new product development and product development lifecycle, including product strategy, development, and launch.