Senior Growth Analyst, Data Science
AffirmWith Affirm, you can pay over time at your favorite brands. No late fees or compounding interest—just a more responsible way to say yes to the things you love.
Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.
We are seeking a highly skilled and experienced Senior Analyst to join our dynamic team, focusing on causal inference and the development of MVP versions of machine learning (ML) models. This role is pivotal in leveraging data science to understand the why behind our data, enabling informed decision-making and driving innovative solutions. The ideal candidate will have a strong background in statistical analysis, causal inference methodologies, and experience in building and iterating on ML models in a fast-paced environment.
What you will do
- Causal Inference Analysis: Apply advanced causal inference techniques to uncover the underlying mechanisms and effects within our datasets, facilitating a deeper understanding of our data's drivers.
- ML Modeling: Develop and refine MVP versions of ML models, focusing on rapid prototyping to test hypotheses and assess potential impact, with an eye towards scalability and integration into larger systems.
- Cross-Functional Collaboration: Work closely with product, engineering, and business teams to define problem statements, identify data needs, and implement solutions that align with organizational goals.
- Data Strategy Development: Contribute to the creation and execution of a comprehensive data strategy, emphasizing causal analysis and ML applications to support data-driven decision-making.
- Insights Communication: Clearly communicate complex analytical findings and model outcomes to non-technical stakeholders, translating data insights into actionable business recommendations.
What we look for
- 3+ years of experience in data analysis, with a strong focus on causal inference and ML modeling.
- Proficiency in statistical software (R, Python) and familiarity with ML libraries (e.g., scikit-learn, TensorFlow).
- Solid understanding of statistical modeling, experimental design, and A/B testing frameworks.
- Exceptional analytical, problem-solving, and communication skills.
- Bachelor's or Master's degree in Statistics, Computer Science, Economics, or a related quantitative field.
Pay Grade - CAN29
Employees new to Affirm or promoted into a new role, typically begin in the min to mid range.
CAN base pay range per year:
Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities.
We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:
- Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents
- Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses
- Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge
- ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount
We believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process.
[For U.S. positions that could be performed in Los Angeles or San Francisco] Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, Affirm will consider for employment qualified applicants with arrest and conviction records.
Tags: A/B testing Causal inference Computer Science Data analysis Data strategy Economics Engineering Machine Learning ML models MVP Privacy Prototyping Python R Scikit-learn Statistical modeling Statistics TensorFlow Testing
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