Staff Data Scientist, Algorithm - Growth Job at Airwallex (San Francisco)

Staff Data Scientist - Algorithms

Join us in building the future of finance. Our mission is to democratize finance...

Location

United States , Menlo Park

Salary:

217000.00 - 255000.00 USD / Year

Robinhood

Expiration Date

Until further notice

Requirements

7+ years of experience applying ML in production, ideally in growth, incentives, marketplace, or personalization domains
Proven track record owning models end-to-end — from design and development to deployment and iteration
Deep expertise in predictive modeling, uplift modeling, causal inference, and experimentation design
Strong technical skills in Python, SQL, and ML frameworks (e.g., scikit-learn, XGBoost, PyTorch or TensorFlow)
Experience working with Airflow and production pipelines, as well as tools for causal ML and experimentation
Excellent product intuition and ability to translate ambiguous goals into measurable algorithmic solutions
Strong communication and stakeholder management skills, with the ability to influence across teams
A collaborative and growth-minded approach, with strong technical leadership and a consistent focus on impact and ROI

Job Responsibility

Build, deploy, and iterate on predictive and causal models for incentive targeting
Design and evaluate experiments to measure incremental impact, payback, and ROI of promotional programs
Partner cross-functionally with Product, Finance, Marketing, and Engineering to inform scalable incentive strategies
Design and optimize incentive allocation algorithms under budget and policy constraints to maximize incremental impact
Monitor production systems, analyze user behavior, and propose algorithmic and policy improvements
Influence the long-term vision for growth modeling at Robinhood, including personalization and cross-sell optimization

What we offer

Performance-driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
100% paid health insurance for employees with 90% coverage for dependents
Lifestyle wallet – a highly flexible benefits spending account for wellness, learning, and more
Employer-paid life & disability insurance, fertility benefits, and mental health benefits
Time off to recharge including company holidays, paid time off, sick time, parental leave, and more
Exceptional office experience with catered meals, events, and comfortable workspaces

Fulltime

Staff Data Scientist - Algorithms

Join us in building the future of finance. Our mission is to democratize finance...

Location

United States , Menlo Park

Salary:

217000.00 - 255000.00 USD / Year

Robinhood

Expiration Date

Until further notice

Requirements

7+ years of experience applying ML in production, ideally in growth, incentives, marketplace, or personalization domains
Proven track record owning models end-to-end — from design and development to deployment and iteration
Deep expertise in predictive modeling, uplift modeling, causal inference, and experimentation design
Strong technical skills in Python, SQL, and ML frameworks (e.g., scikit-learn, XGBoost, PyTorch or TensorFlow)
Experience working with Airflow and production pipelines, as well as tools for causal ML and experimentation
Excellent product intuition and ability to translate ambiguous goals into measurable algorithmic solutions
Strong communication and stakeholder management skills, with the ability to influence across teams
A collaborative and growth-minded approach, with strong technical leadership and a consistent focus on impact and ROI

Job Responsibility

Build, deploy, and iterate on predictive and causal models for incentive targeting
Design and evaluate experiments to measure incremental impact, payback, and ROI of promotional programs
Partner cross-functionally with Product, Finance, Marketing, and Engineering to inform scalable incentive strategies
Design and optimize incentive allocation algorithms under budget and policy constraints to maximize incremental impact
Monitor production systems, analyze user behavior, and propose algorithmic and policy improvements
Influence the long-term vision for growth modeling at Robinhood, including personalization and cross-sell optimization

What we offer

Performance-driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
100% paid health insurance for employees with 90% coverage for dependents
Lifestyle wallet – a highly flexible benefits spending account for wellness, learning, and more
Employer-paid life & disability insurance, fertility benefits, and mental health benefits
Time off to recharge including company holidays, paid time off, sick time, parental leave, and more
Exceptional office experience with catered meals, events, and comfortable workspaces

Fulltime

Staff Data Scientist - Fraud & Risk

We are seeking a skilled and motivated Staff Data Scientist to join our Fraud & ...

Location

United States

Salary:

140000.00 - 210000.00 USD / Year

Socure

Expiration Date

Until further notice

Requirements

Master’s or PhD in Computer Science, Statistics, Applied Mathematics, Data Science, or a related field
or equivalent professional experience
8+ years of experience in data science, machine learning, or related fields, ideally in a high-growth tech or fintech environment
Experience in fraud prevention, risk modeling, or identity verification
Years of hands-on experience developing and deploying deep learning models (such as transformers, CNNs/RNNs, and graph learning)
Experience working with diverse data modalities, such as tabular data, text/language, point clouds, and images
Strong proficiency in Python, SQL, and major ML libraries/frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
Deep understanding of machine learning algorithms, model evaluation techniques, and data pipeline development
Experience with model deployment and monitoring in production environments (specific experience with real-time model inferencing is a plus)
Demonstrated ability to proactively deliver complex outcomes, mentor others, and influence cross-functional decisions

Job Responsibility

Design, develop, and implement advanced deep learning models, including transformers, CNNs/RNNs, and graph learning algorithms, to address complex fraud and risk challenges
Build and optimize models using a variety of input data types, including tabular data, natural language, point clouds, and images
Lead the end-to-end machine learning lifecycle: data exploration, feature engineering, model training, evaluation, deployment, and monitoring in production environments
Take ownership of project outcomes, data quality, and delivery timelines
proactively escalate issues and work collaboratively to resolve challenges
Mentor and share knowledge with peers and junior data scientists, fostering a culture of experimentation, rapid iteration, and continuous learning
Collaborate cross-functionally with Product, Engineering, and Risk teams to define data requirements and drive insights that guide strategic decisions
Conduct in-depth research to explore new data sources and develop novel algorithms that advance the state of the art in fraud detection
Present findings and recommendations to technical and executive stakeholders with clarity and influence
Stay current with advancements in AI and machine learning, applying innovative approaches to real-world problems

What we offer

Offers Equity
Offers Bonus

Fulltime