Lead the design and implementation of causal inference frameworks (e.g., uplift modeling, DML, IVs, DiD, synthetic control) to measure true incremental impact across personalization, marketing, and lifecycle interventions
Establish and standardize methodologies for incrementality, experimentation, and measurement across channels and product surfaces
Build and scale LTV models (user-level and cohort-based), including churn-adjusted and horizon-specific approaches, for real-time decisioning
Develop and deploy personalization models that influence ranking, offer selection, content sequencing, and monetization strategies at the moment of user intent
Ship production-grade machine learning models that directly drive revenue outcomes, including marketplace optimization, partner routing, and budget allocation
Translate predictive outputs (e.g., conversion propensity, incremental CPA, expected LTV) into decision-ready signals for real-time systems
Partner with Data Engineering and Platform teams to define data instrumentation, feature stores (batch and streaming), and model monitoring frameworks (drift, bias, stability)
Influence architectural decisions across modern data and ML platforms (e.g., Snowflake, Databricks, Spark, real-time inference systems)
Provide technical leadership across teams by setting best practices for experimentation, modeling, code quality, and reproducibility
Mentor and develop senior and mid-level data scientists, raising the overall technical bar across the organization
Communicate complex analytical insights and trade-offs to executive stakeholders, translating findings into actionable business strategies
Shape long-term strategy for personalization, experimentation, and AI-driven growth at NerdWallet

Requirements

8+ years of experience in applied machine learning, causal inference, experimentation, or related quantitative fields
Deep expertise in causal inference methodologies (e.g., uplift modeling, doubly robust learners, instrumental variables, difference-in-differences, synthetic control, Bayesian time series)
Proven experience building and operationalizing LTV models for real-time or near-real-time applications
Strong software engineering and production ML experience, including Python (pandas, numpy, scikit-learn, LightGBM/XGBoost) and PySpark, Advanced SQL
Experience with distributed systems and modern data platforms (e.g., Snowflake, Databricks, Spark, AWS/GCP/Azure), Version control and ML lifecycle tools (e.g., Git, MLflow)
Hands-on experience with experimentation frameworks, A/B testing, and statistical diagnostics (e.g., power analysis, bias detection)
Demonstrated success deploying models that materially impact revenue, efficiency, or user experience
Exceptional communication skills with the ability to influence Product, Marketing, Finance, and executive stakeholders
Strong strategic thinking and ability to operate in ambiguous, high-impact problem spaces.
Preferred Qualifications:
Experience with marketplace optimization, ranking systems, or auction-based environments
Familiarity with contextual bandits, reinforcement learning, or sequential decision-making systems
Knowledge of streaming data architectures (e.g., Kafka, Kinesis), orchestration tools (e.g., Airflow, DBT), and feature stores (e.g., Tecton, Feast)
Domain experience in fintech, marketplaces, growth, or performance marketing

Benefits

Industry-leading medical, dental, and vision health care plans for employees and their dependents
Rejuvenation Policy – Flexible Vacation Time Off + 11 holidays + holiday company shutdown
New Parent Leave for employees with a newborn child or a child placed with them for adoption or foster care
Mental health support
Paid sabbatical after 5 years for Nerds to recharge, gain knowledge, and pursue their interests
Health and Dependent Care FSA and HSA Plan with monthly NerdWallet contribution
Monthly Wellness Stipend, Cell Phone Stipend, and Wifi Stipend (Only remote Nerds are eligible for the Wifi Stipend)
Work from home equipment stipend and co-working space subsidy (Only remote Nerds are eligible for these stipends)

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

causal inferenceuplift modelingdoubly robust learnersinstrumental variablesdifference-in-differencessynthetic controlBayesian time seriesLTV modelsPythonAdvanced SQL

Soft Skills

communication skillsstrategic thinkingtechnical leadershipmentoringinfluencing stakeholdersproblem-solvingbest practicesanalytical insightsteam collaborationdecision-making