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. The Title & Launch Management Data Science and Engineering team is at the forefront of driving operational and creative excellence in how we launch and promote content. We build highly automated systems that power the launch of new content formats, such as fast‑follow local broadcasts and video podcasts, and help members discover titles they’ll love. Our interdisciplinary team sits at the intersection of rigorous measurement, advanced analytics, and the latest AI/agentic solutions that enable the ingest, setup, launch, and global distribution of all types of entertainment on Netflix. We are seeking an experienced leader to lead a multidisciplinary team of analytics engineers, data scientists, and machine learning scientists and engineers who build the data products, algorithms, and systems that power title launch. You will guide a highly talented team and partner closely with cross‑functional teams to support Netflix’s growing content offerings and partnerships by automating large‑scale content ingestion and replacing manual processes, developing a flexible, federated system that enables efficient, scalable, and self‑service content ingestion as Netflix continues to grow. Responsibilities - Oversee a diverse portfolio of end-to-end efforts to design, deploy, and rigorously evaluate autonomous AI solutions, advancing Netflix’s title launch strategy and supporting our rapidly expanding slate of content. - Coach, empower, and elevate a team of analytics engineers, data scientists, and machine learning practitioners, increasing their impact and supporting their career development. - Collaborate with a cross-functional team to shape the vision and roadmap for the area, prioritize and drive execution. - Define and cultivate a high standard for both velocity and technical excellence, while nurturing a culture grounded in strong engineering and scientific rigor. - Leverage advanced technical skills and deep product and domain insight to surface new problem spaces and make bold, high-conviction decisions. - Cultivate durable partnerships with stakeholders across product, engineering, and content to align on long-term goals and jointly deliver outcomes. - Act as a visible leader and subject-matter expert, advocating for the team’s work and strengthening its reputation across and beyond Netflix. About you - Proven track record of successfully leading data and ML-focused teams, with a strong emphasis on rigorous measurement and agentic/AI-driven solutions. - Deep expertise in autonomous agentic systems and applied ML, with a demonstrated commitment to staying current on the latest research and having led teams that launch and iterate on production ML services. - Passion for guiding teams through ambiguous, complex technical and business problems, bringing clarity, structure, and disciplined execution. - Strong track record of mentoring and developing talent, including successfully recruiting and growing researchers and engineers across multiple levels. - Master’s or PhD in Machine Learning, Computer Science, or a closely related field. - 6+ years of hands-on ML experience (or 4+ years with a relevant PhD). - 2+ years of experience leading ML teams. - Exceptional verbal and written communication skills, with the ability to influence and align diverse stakeholders. - Deep commitment to driving end-to-end business impact, not just building models. - Netflix culture resonates with you, and you’re excited to model and reinforce it within your team. 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 $676,000.00 - $1,195,000.00. 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.

• Optimizar el capital humano mediante el modelado predictivo de la demanda y la creación de algoritmos de programación de turnos.

• Drive advanced analytics, risk modeling, and compliance solutions • Support CMS Marketplace compliance programs • Blend regulatory analytics, system design, and audit support • Strengthen oversight across QHP issuers and related entities

• Data analysis and modeling: Explore large-scale transactional and behavioral datasets to uncover patterns associated with fraud. • Model development and validation: Build and validate classification models using ML techniques tailored to imbalanced problems (fraud vs. non-fraud). • Feature engineering: Create derived variables that enhance model performance and generalization while avoiding overfitting. • Cross-functional collaboration: Work with engineering, product, and operations teams to ensure seamless integration of models into decision flows. • Monitoring and iteration: Track model performance in production and iterate based on behavioral changes, fraud trends, or strategy shifts. • Research and innovation: Stay up to date on cutting-edge ML techniques for fraud detection in digital transactional environments.