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Director, Data Science
Integral Ad Science
New York, NY, United States
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Job Title: Director, Data Science
City: New York
State: New York or any other unanticipated locations/worksites throughout the US
JOB DESCRIPTION:
1. Work on challenging fundamental data science problems in online advertising.
2. Solid experience with at least one scripting language including Python and with relational databases/SQL.
3. Work with large data sets, and with distributed computing tools (Map/Reduce, Hadoop, Spark, Hive, Pig, HBase, etc.).
4. Lead a small team of data scientists.
5. Establish relationships with other managers in the data science, engineering and product organizations.
6. Operationalize strategy for directly managed team to meet annual and mid-term goals, and contribute to global strategy for entire data science team.
7. Participate in corporate development of methods and evaluation criteria for projects, programs and people. Ensure costs and timelines meet corporate requirements.
8. Persuade leadership on direction and resources. Regular interaction with executives. Perform Vendor assessment and engage in vendor contract negotiation on the technical side.
9. Set up and oversee team best practices for data analysis, instrumentation and experimentation. Assist direct reports with formulation, implementation, testing and validation of predictive models.
10. Coach direct reports in writing high quality efficient code and following coding best practices.
11. Communicate complex quantitative topics to non-technical audiences ranging from executives to marketing/sales.
12. Mentor individual contributors about solving analytical problems using quantitative approaches based on machine learning methods.
13. Mentor junior team members on the following machine learning libraries: scikit-learn, H2O, SparkML, TensorFlow, etc.
14. Lead cross-functional teams as ScrumMaster on very complex projects.
15. Classification of mobile applications for brand safety to scale up a system across multiple categories and languages.
16. Design and guide the implementation of a generalized framework for the management of pre-bid segments for both websites and mobile applications.
17. Must communicate results verbally and in writing.
EDUCATION AND EXPERIENCE REQUIREMENT:
Requires a Ph.D. in Machine Learning, Computer Science, Physics, Mathematics, Statistics, or Related Field and 6 months of experience in the job offered or 6 months of experience in the Related Occupation.
RELATED OCCUPATION:
Sr. Data Scientist performing the following job duties:
1. Work on challenging fundamental data science problems in online advertising using at least one scripting language including Python and relational databases and SQL.
2. Design and implement large scale system for classification of mobile applications into brand safety categories including hate speech, adult content, gambling.
3. Design, implement and test pre-bid segments for programmatic transactions packaging sets of mobile applications, targeting brand safety and fraud violations.
4. Work with large data sets, and distributed computing tools including Map/Reduce, Hadoop, Spark, Hive, Pig, HBase, etc.
5. Measurably impact business KPIs by delivering high quality scalable solutions.
6. Discover actionable insights from data and present them through rich visualizations.
7. Propose and develop solutions independently and in collaboration with others.
8. Write high quality efficient code while implementing own ideas.
9. Responsible for key components of data science driven products.
10. Prepare white papers, scientific publications and conference presentations.
11. Collect new data and refine existing data sources.
12. Assemble data sets from multi-terabyte structured and unstructured data repositories.
13. Formulate, implement, test and validate predictive models.
14. Develop and follow best practices for data analysis, instrumentation and experimentation.
15. Manipulate and analyze complex, high-volume, high-dimensionality data from varying sources.
16. Solve analytical problems using quantitative approaches based on machine learning methods and libraries including scikit-learn, H2O, SparkML, and TensorFlow.
17. Apply practical data science including source control workflows, deploying machine learning models in production, and real-time machine learning.
JOB TIME: Full Time