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 13 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 sales teams to drive the use of Data Science across the Verve Group, collaborate with our audience-focused Data Science team and with the Machine Learning Engineers to engineer prototypes into solutions.
What You Will Do
DOMAIN
In this role your main focus would be on our Ad-Exchange Optimization projects
Win Price Prediction in various auctions setups - 1st price, 2nd price, Waterfall
Supply forecasting — Modeling time dependent supply availability
Demand forecasting — Modeling time dependent demand interest
Inventory valuation — Assessing true value of inventory in open market
Traffic Shaping — Time dependent supply and demand matching
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 campaign and bidding optimization
Discover insights/patterns in exchange data
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 optimization problems
Verifying data quality
