Data Scientists at Direct to Consumer are the insights and modeling partners for the growth, content, marketing, product, and engineering teams at Disney+, Hulu and ESPN+. They use data to empower decision-makers with information, predictions, and insights that ultimately influence the experiences of millions of users worldwide. Scientists on the team build models, perform statistical analysis, and create visualizations to provide scalable, persistent capability that is iteratively improved through direct interaction with cross-functional business partners. 

As a Lead Data Scientist in the DTC (Direct-To-Consumer) Data Science team, you will be partnering closely with the Marketing, Subscriber Analytics, Finance, Business Operations, Commerce Product and Engineering teams to develop models for tackling a multitude of exciting challenges, including content/audience segmentation, customer lifetime value estimation, churn and upgrade prediction, signups and subscribers forecasting, fraud prevention and mitigation, payment optimization, causal inference, anomaly detection and much more! In this role you will also be working very closely with company executives and it requires the use of analytical abilities, business understanding, and technical savviness to identify specific and actionable opportunities to solve existing business problems through data modeling.

We are looking for someone with deep analytical and modeling expertise, a proven track record of thought leadership and eagerness to drive impact. 

Responsibilities

• Modeling: Design, build and improve machine learning models. Work end to end from data collection, feature generation and selection, algorithm development, forecasting, visualization and communicating of model results. Collaborate with engineering to productionize models. Drive experimentation to test impact of model based optimization. 

• Deep analysis: Develop comprehensive understanding of subscriber and payment data structures and metrics. Mine large data sets to identify opportunities for driving growth and retention of subscribers. 

• Visualization of Complex Data sets: Development of prototype solutions, mathematical models, algorithms, and robust analytics leading to actionable insights communicated clearly and visually.  

• Partnership: Partner closely with business stakeholders to identify and unlock opportunities, and with other data teams to improve platform capabilities around data modeling, data visualization, experimentation and data architecture. 

Basic Qualifications

• Bachelor’s in Advanced Mathematics, Statistics, Data Science or comparable field of study. 

• 7+ years of experience designing, building, and evaluating practical machine learning solutions 

• Strong coding experience in one (or more) data programming languages like Python/R, additional experience with scientific libraries like Numpy, Pandas, or equivalent libraries a plus. 

• Strong background in statistical modeling: regression, time series analysis and other techniques. 

• Experience developing scalable mathematical models and solving complex quantitative problems that can be understood by non-mathematical colleagues. 

• 7+ years experience with databases and data pulling tools (SQL, Vertica, Hive). 

• Willingness to adapt in fast-paced and quickly growing work environment. 

• Seasoned & resourceful problem solver who figures out how to get things done, even if it means navigating through ambiguity 

Preferred Qualifications

• Advanced degree (Master’s or Ph.D.) in a quantitative discipline 

• Excellent analytical skills, advanced level of statistics knowledge 

• Strong expertise with Python and libraries such as scikit-learn, scipy *

• Familiarity with Bayesian modeling and probabilistic programming packages such as PyMC 

• Familiarity with data platforms and applications such as Databricks, Jupyter, Snowflake, Airflow, Github 

• Familiarity with data exploration and data visualization tools such as Tableau, Looker 

• Familiarity with designing and analyzing A/B testing and other experiment types 

• Demonstrated skills in selecting the right statistical tools given a data analysis problem 

• Ability to adapt quickly in a fast-moving environment with shifting priorities 

• Strong communication skills, for both technical and non-technical audiences 

• Ability to handle multiple tasks concurrently and in a timely manner, including large and complex ones 

• Demonstrated leadership experience, including people and project management 

• Experience in advanced ML techniques (neural nets, NLP, image processing) 

• Experience thinking strategically to interpret market and consumer information, preferably about a subscription service.

Additional Information

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