Data Scientist, Marketing

Key Skills

Java Programming, Python Programming, SQL, Tableau, Apache Hive, CSS3, SPARK Programming, Google sheets, ETL frameworks

Job Description

Marketing data science informs decisions across Uber's global marketing efforts, accelerating both demand and supply growth worldwide. We leverage advanced statistical modeling, machine learning, or data mining techniques in a scalable manner including large scale data processing such as Spark, Hive, and Uber's proprietary machine learning platform, and more.

About the role
In this new role, you will collaborate with a cross-functional team consisting of engineering, product, marketing, and operation managers to build impactful datasets, repeatable processes for extracting value from our huge quantity of marketing data, create monitoring systems and analyze data. You should have a demonstrated ability to make sense out of large quantities of/ data and build systems to synthesize and simplify that data. You should have comfort working with different stakeholders like marketing, engineering and product teams.

You possess a passion for improving techniques, processes, tracking, analytical insights, and technology used by marketing to achieve our bold goals. You will help address the complex challenges of reporting, experiment design, budget allocation, marketing effectiveness, return on investment, user behavior, and channel effectiveness using state-of-the-art machine learning models.

Responsibilities:

• Develop and manage a scalable and reliable global marketing data platform
• Work with engineering and marketing technology to build, maintain and operate the data pipelines including external integrations (API, S3, G.Sheets, etc) and internal ETL (Hive)
• Develop Data Quality checks for source and target data sets.
• Develop UAT plans and conduct QA
• Build data models, tables, data visualizations, and automated analytics & reporting that operationalize insights for marketings
• Help understand our data by performing exploratory and quantitative analytics, data mining, and discovery.
• Translate marketing requirements into technical specifications
• Develop methodologies for Brand marketing measurement
• Assess vendor's measurement capabilities and identify the most useful data sources for Brand measurement

Requirements:

• 3+ years of work experience in data science, data architecture, data modeling, technical solutions, business intelligence, engineering or other technical positions.
• 3+ years of SQL and Python experience
• 3+ years' work experience implementing and reporting on business key performance indicators in data warehousing environments.
• Strong understanding of data modeling principles including Dimensional modeling, data normalization principles etc.
• Thorough understanding of data capabilities offered by publishers including ad products, brand lift and measurement solutions (ie. Programmatic, Facebook, Paid Search, Youtube, Audio, etc)
• Excellent quantitative skills and comfort with data stores , ETL & querying frameworks (Spark, Hive) and producing technical stacks
• Proficiency with Tableau or other dashboard building tool (Visualising the data for customer understanding)

• Excellent organizational skills and attention to detail.
  BS/MS in Engineering, Computer Science, Math, Economics, Statistics, Advertising, Marketing or other disciplines with demonstrated technical exposure

At Uber, we reimagine the way the world moves for the better. The idea was born on a snowy night in Paris in 2008, and ever since then, our DNA of reimagination and reinvention carries on. We've grown into a global platform moving people and things in ever-expanding ways, taking on big problems to help drivers, riders, delivery partners, and eaters make movement happen at the push of a button for everyone, everywhere.

We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let's move the world forward, together.