• Transform revenue data into AI-powered intelligence that drives smarter, faster, and more predictive decision-making across the business. • Lead the application of advanced analytics, machine learning, and generative AI to uncover patterns in sales performance, customer behavior, and revenue dynamics. • Build scalable predictive models and embed AI into revenue workflows to help forecast growth, reduce churn, optimize sales strategies, and identify high-value opportunities. • Directly influence executive decision-making, accelerate insight generation, and position AI as a core driver of measurable business impact across the revenue organization. • Conduct in-depth analysis of sales cycles, conversion rates, customer churn, and other revenue-driving activities using advanced AI-powered analytics tools and large-scale data processing techniques. • Leverage generative AI, machine learning platforms, and automated analytics solutions to accelerate insight generation and improve analytical depth and speed. • Translate complex, AI-derived data findings into clear, concise, and compelling narratives for various stakeholders. • Identify opportunities where AI can create measurable business impact and collaborate with stakeholders to define business problems and translate them into AI-powered data science solutions.

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next. A culture of experimentation enables Netflix to continuously evolve and improve our products, delivering more joy to existing members and attracting new members from around the globe. Because experimentation is so pervasive at Netflix, we continually enable new capabilities and onboard new initiatives to the platform with close cross-functional collaboration with our Data Science partners. At the nerve center of experimentation at Netflix is our internal, Netflix-wide, Experimentation Platform, responsible for democratizing trustworthy experiments across the company and enabling new capabilities across the company. We are looking to expand the number of full-stack data science faculty on the team to better support experimentation best-practice and drive further innovation in data science tooling. Responsibilities: - Work closely with data science and engineering stakeholders to act as a strategic thought partner and onboard new workflows. - Design, build, operate, and maintain mathematics and engineering implementations of methodologies employed by the experimentation platform, as well as facilitating data science partner contributions. - Maintain existing inference related data and analysis pipelines, including compute clusters and API services. - Develop new tooling to meet emerging experimentation-related business needs. - Proactively work with the rest of the XP team (including other full-stack data scientists) to ensure that our platform continues to deliver trustworthy and useful analysis results. Qualifications: - Advanced degree (PhD or Masters) in Computer Science, Statistics, Economics, Applied Mathematics, or related quantitative field - 3+ years experience with statistics / inference, ideally in an experimentation context. - 3+ years experience with data science languages, ideally including Python and SQL. - 3+ years building and operating applications serving internal or external production traffic - Ability to translate mathematical concepts into clean maintainable engineering code. - Curiosity to learn new statistics, methods, optimization techniques, etc. - Excellent communication and collaboration skills. - Able to bring clarity and life to ambiguous problems. Curious to learn more about our experimentation culture at Netflix? Read up on experimentation culture at Netflix and browse the Netflix Research page. Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $372,000.00 - $600,000.00. This compensation range will vary based on location. Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here. Netflix is a unique culture and environment. Learn more here. Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner. We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service. Job is open for no less than 7 days and will be removed when the position is filled.

• Serve as the development team’s primary liaison with research, country and programs teams to secure impact indicators, KPIs, and performance data. • Maintain an updated repository of organizational metrics in key impact areas including learning gains, climate benefits, and livelihoods for proposals, reports, and donor materials and external communications. • Ensure data accuracy, consistency, and timeliness across country programs and reporting cycles. • Collaborate with finance team to retrieve high-level financial data (e.g., cost-per-student, efficiency metrics, budget trends). • Support development colleagues in interpreting and presenting financial data clearly for donor audiences. • Collect and synthesize qualitative inputs from country offices, including best practices, lessons learned, and solution development for common challenges for both internal and external use. • Maintain systems for sharing and accessing qualitative insights across the development team. • Work with the communications and development teams to produce coherent narrative summaries and programmatic context for proposals, donor reports, and briefings. • Translate technical data and research findings into clear and accessible formats for external audiences. • Create simple charts, tables, and data visualization. • Draft supporting narrative content when needed in collaboration with grant writers. • Maintain organized data dashboards and ensure information is easily retrievable for development staff. • Track data needs for active proposals, reports and narrative priorities. • Support cross-team planning related to evidence, reporting, and knowledge management.

• Designs and develops of scalable solutions using AI tools and machine-learning models • Performs research and testing to develop machine learning algorithms and predictive models • Utilizes big data computation and storage tools to create prototypes and datasets • Conducts model training and evaluation • Integrates, tests, tunes, and monitors solutions • Critically evaluate valuation processes to identify gaps or invalid assumptions • Independently design and implement mathematically rigorous methodologies for measuring program efficacy • Extract and manipulate large-scale healthcare data (claims, clinical, SDoH) to build independent, scalable valuation and predictive models • Act as a strategic communicator, translating complex methodologies and financial impacts into compelling narratives for non-technical executive stakeholders • Develop operational frameworks to translate clinical interventions into defensible financial forecasts and enterprise-wide statements • Navigate extreme ambiguity to scope undefined business problems and transform raw data into production-ready valuation frameworks without an existing playbook • Analyzes use cases of ML algorithms and apply findings to enable business decisions by deriving insights through data visualization • Writing complex SQL and Python/R code to extract data from a variety of databases and data sources • Conduct exploratory data analysis from complex data sources and build key data sets to support company mission operational analysis • Builds complex ML/AI models using common methods within R and Python • Analyzes the ML algorithms that could be used to solve a given problem and rank them by their success probability • Develop logging, alerting, and mitigation strategies for handling model errors • Evaluate and design experiments to monitor key model metrics and identify improvement opportunities • Produces fairness review for each developed model, to ensure model is free of bias • Set up continuous integration/continuous deployment (CI/CD) pipelines used for model automation • Model deployment with containers such as Kubernetes and/or Docker • Produce clean code that has been properly unit tested and optimized for performance • Completes proper deployment integration testing to ensure seamless deployment • Performs other duties as assigned