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Our Corporate Finance team, part of Finance Data Science and Engineering (DSE), brings this same innovative approach to our financial operations. As trusted partners to our finance stakeholders, we analyze key metrics, surface actionable insights, and drive value for the business. As Netflix continues to grow, the need for advanced analytics in Finance is greater than ever – leveraging cutting-edge techniques to uncover deeper insights and identify new opportunities for impact.
You’ll play a key role in shaping the future of advanced analytics within Corporate Finance at Netflix, helping to unlock new value and drive strategic decision-making through data. Learn more from our blog on how Finance AEs are driving the business forward through data.
In This Role, You Will
- Partner with Analytics Engineering, Data Science, Data Engineering, and cross-functional Finance teams to uncover data opportunities where advanced analytics (think: forecasting, classification) can provide actionable insights.
- Lead advanced analytics projects in key Corporate Finance areas (e.g., spend classification modeling, forecasting general ledger categories, and other greenfield opportunities in procurement, operations, and strategy).
- Create and curate robust datasets and data pipelines from structured finance data sources (revenue, expenses, accounting journal entries, vendor invoices) to unlock insights and identify patterns.
- Productionize analytics models in partnership with Data Science and Data Engineering teams, focusing on practical, scalable solutions.
- Help upskill the team on when and how to use classical machine learning and modeling techniques, and when simpler analytics approaches are more appropriate.
- Demonstrated ability to write clean, readable, and impactful SQL code.
- Strong proficiency in Python (including notebooks and modeling skills).
- Advanced SQL skills for analytics data processing (e.g., splitting data into train/eval/test sets).
- Experience with AI tools for automation (e.g., Cursor, Claude, Commands, Rules) is a plus.
- Familiarity with DBT or other semantic data modeling tools for strong data foundations
- 3+ years of experience in analytics engineering, data science, or a similar data-focused role, ideally with a strong focus or interest in finance.
- Solid statistical foundation, or a strong interest in upskilling in statistics and classical machine learning.
- Examples of applying classical ML techniques (such as classification or forecasting) to real-world problems, especially in the finance domain, are a strong plus.
- Excellent communication skills, with the ability to translate complex data insights into clear, actionable recommendations for both technical and non-technical stakeholders.
- Collaborative approach and a passion for continuous learning and innovation.
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 detail about our Benefits here.
Netflix has 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.
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