About Dave
Dave is a financial app on a mission to build products that level the financial playing field. It is redefining the financial landscape by leveraging technology to create an affordable, transparent, and user-centric access to liquidity for millions of Americans. As a leading innovator in the U.S. financial services sector, Dave’s digital financial platform offers products designed to meet the credit needs of those underserved by traditional financial institutions. Dave’s offerings include its flagship ExtraCash product, providing members up to $500 within minutes. The company is on track to launch several new product offerings in 2026, including a Buy Now Pay Later (BNPL) option. Dave is focused on serving Americans who are financially vulnerable or living paycheck to paycheck. Dave is leading the charge in creating a new era of credit products that prioritizes speed, affordability, and accessibility, making it the go-to financial partner for those who need it most.
About the Role
We’re looking for a Lead Machine Learning Scientist to own and scale ML-driven Marketing/Growth/Product capabilities at Dave. This role will drive how we use data and machine learning to improve acquisition, engagement, retention, and monetization across a multi-product ecosystem. You will help identify high-impact opportunities, building production-grade models, and shaping the roadmap for ML in Marketing/Growth. This is a highly strategic and hands-on role requiring both technical depth and business acumen.
Responsibilities
ML Strategy for Marketing/Growth
- Architect and lead machine learning solutions across teams, driving multi-person, cross-functional initiatives.
- Define and execute the roadmap for applying ML to improve marketing efficiency and growth.
- Proactively identify high-impact opportunities where ML can drive step-function improvements (not just respond to requests).
- Partner closely with Marketing, Product, and Finance to align ML investments with business priorities.
Build High-Impact Models
- Lead development and deployment of core models, including:
- Propensity (conversion, engagement)
- Churn prevention and intervention
- Customer Lifetime Value (LTV) models, co-develop with Finance to align on assumptions, definitions, and business use cases
- Cross-sell / next-best-action models across Dave’s products
- Improve onboarding, targeting, personalization, and segmentation at scale.
- Work across modeling lifecycle from problem formulation, training, calibration, and iteration in production.
- Build agentic engineering workflows that accelerate development, testing, and documentation.
Data & Platform Leverage
- Analyze large and complex dataset; Identify and evaluate high-leverage internal and external data sources to improve model performance.
- Build business cases for new data acquisition and lead onboarding efforts.
- Ensure models are scalable, measurable, and tightly integrated into marketing workflows.
Optimization & Efficiency
- Continuously evaluate and improve marketing spend efficiency through ML-driven insights and models.
- Identify and resolve cost inefficiencies across models and pipelines.
- Design and optimize reward and incentive strategies (e.g., referral incentives, promotional offers) to maximize user acquisition, activation, and retention while managing cost efficiency.
- Develop models to determine optimal reward levels and targeting, balancing conversion lift with incremental cost to improve ROI.
- Evaluate and measure incrementality of rewards and promotions using experimentation (A/B testing, uplift modeling) to ensure incentives drive true behavioral change rather than subsidizing existing demand.
Technical Excellence
- Set standards for model development, experimentation, and validation.
- Promote adoption of modern ML techniques, leverage pre-trained models, including effective use of AI/LLMs where applicable.
Experience Required
- 7+ years of experience in machine learning, data science, or a related field.
- Proven experience building and scaling ML models in production environments.
- Strong experience with marketing-related models (propensity, churn, LTV, targeting, etc.).
- Demonstrated ability to lead large, ambiguous, cross-functional projects.
- Strong programming skills (Python, SQL) and experience with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
- Familiarity with Marketing KPIs (CAC, ROAS etc).
- Strong communication skills—you can translate between technical and business audiences.
Experience Preferred
- Experience in fintech or multi-product ecosystems.
- Familiarity with attribution, MMM, or marketing measurement.
- Experience with large-scale data platforms (e.g., Snowflake).