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Data Scientist II

extra holidays - extra parental leave

Remote:

Full Remote

Experience:

Mid-level (2-5 years)

Work from:

New York (USA), United States

Offer summary

Qualifications:

Master’s degree in Statistics, Economics, Mathematics., 2 years of experience in a related position., Proficient in R, Python, and SQL., Experience with marketing mix models using regression techniques..

Key responsabilities:

Analyze data on music listening preferences.
Communicate insights to stakeholders effectively.

Framework Startup https://frame.work/

11 - 50 Employees

See more Framework offers

Job description

Duties:

Work with local affiliates/labels, international teams, and Global Technology to understand the trends and dynamics driving major streaming services from the perspective of the fans and listeners who enjoy WMG music.

Create expert level insight into audience behavior by helping to establish best practices for data which captures music listening preferences to gauge the impact of marketing initiatives and campaigns on our listeners, and to comprehensively understand and predict listener behavior.

Work with quantitative data to coordinate with labels, analysts and other business stakeholders on delivery of reporting and inference. Develop new forecasting and audience targeting procedures.

Communicate findings and insights to key stakeholders to establish best practices, and to guide analysis into action and results.

Transform data into stories via tailor made presentations and develop new hypothesis testing and decision making for real time assessment of audience viewing patterns and performance of music delivery streams.

Interact with data engineering teams to identify the data needs for the mathematical models.

Requirements:

Master’s degree or foreign equivalent in Statistics, Economics, Mathematics or a related quantitative field and 2 years of experience in the position offered or related.

Must have 2 years of experience with: working with R, Python, and SQL; using advertising data to build marketing mix models to measure incremental impact of ads and campaigns, using techniques like multi-linear regression and Hierarchical Bayes

Gathering and analyzing audience data and using targeting models including propensity scoring, and look-alike modeling to develop actionable insight into audience behavior deploying machine learning models into a production environment

Communicating technical concepts using data visualizations (using libraries such as matplotlib or ggplot2); and preparing written presentations of technical concepts for a non-technical, business stakeholder audience.