Associate Director, Data Science

Horizon Media•New York, NY

1d•$125,000 - $165,000•Hybrid

About The Position

The Associate Director, Marketing Sciences is at the center of data, media and brand strategy. They act as a day-to-day contributor on specific client teams and focus on the generation of insights to inform audience strategy for planning and analysis of performance and optimizations while campaigns are still inflight. Key responsibilities are working with big data (data extraction & transformation) as well the application of advanced statistics and machine learning techniques to accomplish a variety of innovative and investigative projects on behalf of Horizon agencies and their clients. The role requires hands-on analytics expertise and client-facing solution design and will influence Horizon data strategy and product direction. This role often requires an ability to work cross-functionally with Client Architects and other teams key to data acquisition through client presentation, as a result, a collaborative spirit and strong communication skills are required. They will be expected to become expert in data assets within Horizon’s proprietary Connected Marketing Platform called blu. and take ownership of specific data sources and client accounts. This role will report to a Vice President, Marketing Science, blu.

Requirements

BS/BA degree in Computer Science, Statistics, Applied Mathematics, or a related field required.
3+ years relevant experience, preferably in a media, marketing or digital advertising environment.
Working knowledge of Big and basic data mining routines a must.
Expert knowledge of SQL/Python /R knowledge a must.
Solid understanding of data technology integrations across data sources and ecosystems, and ability to troubleshoot.
Ability to work in a fast paced, multiple project environment on an independent basis and with minimal supervision.
A team player who can work collaboratively within the group and across business units/functions.
Excellent organizational, communication and interpersonal skills.

Nice To Haves

Advanced Statistics a plus.
Knowledge of GitHub a plus.
Machine Learning a plus.
Experience working with digital analytics and audience insights reporting (Campaign Manager/ GA/ Adobe/GA360) - a plus.

Responsibilities

Operate as a Product Innovator and become a Product expert and power user, to drive continuing improvement in both in the analysis and results being supported by AI and the continuing custom solutions built on top and around those
Experience with the ongoing operation and support of a Product in development and/or production
Ability to ensure design principles that scale and can be repeatable and/or extensible to other clients or industries
Experience with release management processes, including all levels of testing (Alph, Beta, QA, UAT)
Ability to define features, models, queries and requests and provide “proof of concept”s for Product enhancement and future incorporation into blu+ product
Ability to understand and leverage blu. structured and unstructured data
Data transformation/mining and generation of insights to explain audience performance and inform optimization
Data join process between blu audiences, media exposure and conversion events to build feedback loops
Analysis of audience conversion to KPIs
Model creation to estimate contribution of attributes driving conversion
Validation of models used during the Audience Building process
Assessment of audience duplication and incrementality by channel, partner, or tactic
User/log level data extraction from delivery sources to create new audiences based on exposure to manage frequency
Database(s) querying to extract exposure events and create a new audience based on exposure by paid media channel (digital or linear)
Problem solving and development for new modeling and analysis techniques
Increasingly utilize AI solutions to inform and expedite some or all of the required analysis
Advanced knowledge of dimensions & metrics (people based, behavioral and media data)
Ability to understand and make sense of the relationships/connections across datasets
Knowledge of the data dictionaries and taxonomies of structured data sets
Experience in ML and different techniques of supervised and unsupervised learning
ETL & EDA processes
Understanding of the complexity of working with Big Data
Act as a collaborative partner to the business and all stakeholders
Participate in daily standups and meet delivery commitments
Proactively identify and, when required, escalate issues and blockers striving to avoid business/client impact
Work with Client Architects and Data Solutions to outline work required to fulfil requests and generate outputs
Provide input to team training and development needs