Reporting directly to the CEO, you will own cross-departmental data projects at CoâStar, using scientific and statistical methodologies to inform the evaluation and execution of key product strategies.
Your key responsibilities will be:
1. Leverage user behavior data to develop, test, validate, build, and operationalize innovative predictive models independently.
- Identify appropriate data science techniques, tools, and applications to develop algorithms â including statistics, machine learning, data and text analytics, clustering analysis, pattern recognition and extraction, automated classification and categorization.
- Maximize the effective and intelligent use of these tools.
- Work closely with engineering to prioritize collection, operationalize models, and deploy solutions in an iterative and agile continuous integration and delivery environment using lifecycle management methods and tools.
- Work closely with engineering to orchestrate storage and infrastructure. Own planning and usage of data pipelines and existing big data technologies for storage and accessing (AWS) and processing (hadoop, apache spark, other mapreduce frameworks).
2. Use scientific and statistical tools to optimize decision-making and strategy formulation.
- Use data to understand tangible business drivers, prioritize KPIs, and drive optimized decision-making. Interpret data to provide actionable insight with real, immediate impact and demonstrable business value.
- Synthesize and communicate findings to inform business decisions and to foster a results-driven culture.
- Drive data-driven experimental methods across departments. Help team design experiments, test hypotheses, and objectively measure outcomes using quantitative methods.
Qualifications
- 10+ years experience independently designing & implementing state of the art predictive algorithms, including data management/processing (ETL)
- PhD in Stats, Math, CS, or another quantitative field focused on data and its use
- Expert-level understanding of core principles & modern tooling in data science, including machine learning (eg SK Learn, Pytorch, Tensorflow), data and text analytics, clustering analysis, pattern recognition and extraction, automated classification and categorization, deep learning, and NLP
- Experience using data-driven insights to manage and mentor across departments, particularly with creatives and creative applications
- Knowledge of applied statistics and big data techniques relevant to emotion and the arts.
- Track record of publications in machine learning/statistics journals or success in competitions like KDD.
Find one job in US 