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Immediate Hiring: Data Scientist II

Remote role Full-time Open position

Invest in your future with this career-defining role as a Data Scientist II! Embrace a modern work style with this fully Remote opportunity. This position requires a strong and diverse skillset in relevant areas to drive success. The compensation for this role is benchmarked at a competitive salary.

 

 

About The Team Marketing science – a sub-team within marketing analytics at Disney’s Direct to Consumer team (Hulu, Disney+, ESPN+ and Star) – is in search of an econometrician to run marketing mix models (MMM) and associated ancillary analysis. This position will work as part of a team focused primarily on econometric modeling, which also provides support for downstream practices used to inform marketing investment. The analyst plays a hands-on role in modeling efforts. The ideal candidate has a substantial quantitative skill set with direct experience in marketing science practices (MMM, attribution modeling, testing / experimentation, etc.), and should serve as a strong mentor to analysts, helping to onboard new talent in support of wider company goals. Technical acumen as well as narrative-building are integral to the success of this role. Responsibilities • Build, sustain and scale econometric models (MMM) for Disney Streaming Services with support from data engineering and data product teams • Quantify ROI on marketing investment, determine optimal spend range across the portfolio, identify proposed efficiency caps by channel, set budget amounts and inform subscriber acquisition forecasts • Support ad hoc strategic analysis to provide recommendations that drive increased return on spend through shifts in mix, flighting, messaging and tactics, and that help cross-validate model results • Provide insights to marketing and finance teams, helping to design and execute experiments to move recommendations forward based on company goals (e.g., subscriber growth, LTV, etc) • Support long-term MMM (et.al.) automation, productionalization and scale with support from data engineering and product • Build out front-end reporting and dashboarding in partnership with data product analysts and data engineers to communicate performance metrics across services, markets, channels and subscriber types Basic Qualifications • Bachelor’s degree in advanced Mathematics, Statistics, Data Science or comparable field of study • 3+ years of experience in a marketing data science / analytics role with understanding of measurement and optimization best practices • Coursework or direct experience in applied econometric modeling, ideally in support of measure marketing efficiency and optimize spend, flighting and mix to maximize return on ad spend (i.e., MMM) • Exposure / understanding of media attribution practices for digital and linear media, the data required to power them and methodologies for measurement • Understanding of incrementality experiments to validate model recommendations and gain learnings on channel/publisher efficacy • Exposure to / familiarity with with BI/data concepts and experience building out self-service marketing data solutions • Strong coding experience in one (or more) data programming languages like Python/R • Ability to draw insights and conclusions from data to inform model development and business decisions • Experience in SQL Preferred Qualifications • Masters degree in Computer Science, Engineering, Mathematics, Physics, Econometrics, or Statistics Additional Information #DISNEYTECH The hiring range for this position in New York, NY is $120,400 to $161,500 and in Santa Monica, CA is $115,000 to $154,000 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered. Apply Job!

 

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