Data Science Manager

2 months ago

San Mateo, California, United States Zeta Global Full time

WHO WE ARE

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry's largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world.

THE ROLE

  • Data Science Manager wanted in San Francisco, CA to lead data science initiatives and guide a team of data scientists to drive data-driven decision-making across the organization.
  • Oversee the end-to-end data science process, collaborate with business stakeholders, and ensure the successful deployment of machine learning models to deliver measurable business value.
  • Work closely with business stakeholders to identify opportunities where data science can provide solutions to complex problems and drive business outcomes.
  • Collaborate with cross-functional partners to foster a data-driven culture within the organization. Lead the data gathering and investigation process, ensure data quality and relevance for machine learning model development.
  • Conduct exploratory data analysis to gain insights and inform data pre-processing and feature engineering.
  • Oversee the data cleaning process to ensure data integrity and quality.
  • Manage the application of data science techniques, including machine learning, statistical modeling, and artificial intelligence, to develop models that can make predictions on unseen data. Collaborate with a team of data scientists to test model accuracy and performance, and continuously improve model iterations.
  • Work closely with engineering team members to deploy trained models into production environments.
  • Present key findings and results to business stakeholders and incorporate their feedback into model improvements.
  • Perform post-deployment measurement and analysis to determine the business value delivered by the models.
  • Review and provide guidance on the work of other team members, ensure adherence to best practices and industry standards.
  • Design and execute experiments to measure the impact of new features and machine learning models on business outcomes.
  • Use machine learning techniques, data analysis tools such as Spark, ML frameworks such as scikit learn, tensorflow and pytorch & languages such as Python and SQL.

Option to telecommute two days a week.

Bachelor's degree in Computer Engineering, Computer Science or Data Science plus 5 years of experience in the job offered or in similar occupation.

Salary expectations: $220,500/yr

Send resume to: G Avanzato, Zeta Global Corp., 3 Park Avenue, 33rd Floor, New York, NY 10016.

BENEFITS & PERKS

  • Unlimited PTO
  • Excellent medical, dental, and vision coverage
  • Employee Equity and Stock Purchase Plan
  • Employee Discounts, Virtual Wellness Classes, and Pet Insurance

And more

PEOPLE & CULTURE AT ZETA

Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual's sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.

We're committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here:

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