Staff Data Scientist
Who We Are
Verve has created a more efficient and privacy-focused way to buy and monetize advertising. Verve is an ecosystem of demand and supply technologies fusing data, media, and technology together to deliver results and growth to both advertisers and publishers–no matter the screen or location, no matter who, what, or where a customer is. With 30 offices across the globe and with an eye on servicing forward-thinking advertising customers, Verve’s solutions are trusted by more than 90 of the United States’ top 100 advertisers, 4,000 publishers globally, and the world’s top demand-side platforms. Learn more at www.verve.com.
About the Role
In this role you will work closely with product, engineering and other teams, collaborate with other Data Science team and with the Machine Learning Engineers to engineer prototypes into solutions.
Domain
In this role your main focus would be on our Audience generation and insight projects
Audience generation: ML for embedded targets, audience privacy first approaches, Composite AI agents, etc..
Audience insight - describe audience composition and characteristics
Adhoc analysis - support business request to better understand our data assets when data is not readily available from our BI tools
Support sales pitch - provided valuable extra insight to augment the value proposition for key accounts
What You Will Do
Research and Development
Our Data Science role includes the following responsibilities:
Research and development of cutting edge Machine Learning systems, models, and schemes in many different areas of Adtech
Develop real-time algorithms for audience creation and segmentation
Discover insights/patterns in our customers from various data sources such as exchange data, behavioural data, location data, 1 and 3rd party data assets
Design experiments, oversee A/B testing, evaluate the quality of derived assets and continuously monitor model performance
Create proof of concepts and data science prototypes
Search and select appropriate data sets
Perform statistical analysis and use results to improve models
Identify differences in data distribution that could affect model performance in real-world situations
Visualize data for deeper insights
Analyze the use cases of ML algorithms and ranking them by their success probability
Understanding when your findings can be applied to business decisions
Reducing business problems into Machine Learning problems and opportunities
Verifying data quality
Collaborative Work
Work closely with product, engineering, and sales teams to drive the use of Data Science across the Verve Group
Collaborate with our Exchange Data Science team
Collaborate with Machine Learning Engineers to engineer prototypes into solutions
