About the Team

As a Senior+ Agentic Analytics Engineer at Opendoor, you will join the Data organization and help build something that doesn't exist at most companies: an Agentic Analytics team. This is a high-impact IC role at the intersection of data science, data engineering, and AI.

Opendoor has adopted a "Default to AI" philosophy—if AI can handle it, AI should handle it. We're not theorizing about AI transformation; we're shipping it.

Why This Role

Most analytics teams are stuck in reactive mode—fielding ad-hoc requests and building dashboards that go stale. We're building something different: AI systems that proactively tell leaders what they should focus on, why it matters, and what to do about it.

If you want to define what analytics looks like in an AI-first company, this is it.

What You'll Do

Build Proactive Intelligence Systems

• Design and deploy AI agents that monitor business performance and surface actionable insights without being asked

• Architect systems that shift analytics from "answering questions" to "anticipating needs"

• Create intelligent alerting that distinguishes signal from noise and recommends specific actions

Create AI-Powered Analytics Tools

• Develop natural language interfaces that let business leaders query data conversationally

• Build semantic layers that translate business concepts into data structures optimized for LLM reasoning

• Ship internal tools that transform how Opendoor makes decisions at every level

Own the Analytics-to-Action Pipeline

• Design data models that support automated analysis, pattern detection, and AI agent interactions

• Build workflows that connect insight generation to recommended actions

• Establish feedback loops that make our AI systems smarter over time

Drive Leverage Across the Business

• Identify high-value opportunities where AI can augment human decision-making

• Implement end-to-end solutions using Claude, custom agents, and modern AI tooling

• Measure impact rigorously—we care about decisions influenced, not dashboards built

We're Looking for Teammates Who Have

Technical Foundation

• 5+ years of experience in analytics, data science, or data engineering, with increasing scope and ownership

• Expert-level SQL with demonstrated ability to design complex data models and optimize for both human and AI consumption

• Strong Python proficiency for data engineering, automation, and AI agent development

• Experience with modern data warehouses (Snowflake, BigQuery, or similar) and the broader data stack (dbt, Airflow, etc.)

AI & Automation Expertise

• Hands-on experience building and deploying AI agents or LLM-powered applications in production

• Deep understanding of LLM capabilities, limitations, and prompt engineering best practices

• Experience architecting semantic layers or natural language interfaces for data access

• Proven ability to identify high-ROI automation opportunities and ship end-to-end solutions

Analytics & Business Acumen

• Track record of driving measurable business impact through analytics—not just producing analyses, but influencing decisions

• Strong analytical intuition—you know what questions matter before they're asked

• Experience translating ambiguous, cross-functional business problems into technical solutions

• History of operating autonomously and setting direction, not just executing on defined projects

Leadership & Influence

• Experience mentoring other analysts or engineers, formally or informally

• Demonstrated ability to influence senior stakeholders and drive alignment across teams

• Comfort operating in ambiguity and shaping strategy, not just responding to it

• Track record of building systems and processes that scale beyond yourself

Builder Mindset

• You ship products, not just dashboards 

• You'd rather build something useful in a week than something perfect in a quarter

• You measure success by business impact, not technical complexity

• You're energized by creating leverage—building once so others benefit forever

Bonus Points If You Have

• Experience in real estate, fintech, or marketplace businesses

• Background in marketing analytics, sales operations, product analytics, or pricing/economics

• History of building tools or platforms that replaced recurring analyst work

• Experience leading technical projects or small teams

Data Scientist, Pricing

Location: In office 4 days a week; Ontario, Canada.

Opendoor is transforming one of the largest, most complex markets in the world — residential real estate — using data at massive scale. Every pricing signal we generate directly impacts how we value homes, how we manage risk, and how efficiently capital moves through our marketplace. The work is highly leveraged: the quality of our pricing decisions influences conversion, margins, customer trust, and the company’s financial performance.

We are looking for mid to senior level Data Scientists. In this role, you will be a core driver of how Opendoor prices real estate at scale. You’ll operate at the intersection of economics, machine learning, experimentation, and product strategy — tackling ambiguity, shaping the pricing roadmap, and building models/analyses that materially move the business. Your insights will influence how we evaluate millions of dollars of housing inventory — and directly shape outcomes for our customers, our balance sheet, and the health of our marketplace.

What You’ll Do

• Build and maintain pricing metrics, dashboards, and frameworks.
• Run experiments and causal analyses to measure impact and drive decisions.
• Develop predictive + statistical models that improve pricing accuracy.
• Partner closely with Product, Engineering, and Operations teams to influence roadmap and model deployment.
• Deliver insights and narratives that inform executive strategy.

Skills & Qualifications

• Deep statistical reasoning: hypothesis design, experimental design, causal inference, and ability to distinguish signal vs noise.
• Proven end-to-end ML ownership: data acquisition, feature engineering, model development, validation, deployment, and ongoing monitoring.
• Strong SQL + Python proficiency; comfortable working with production data pipelines and modern ML tooling (e.g., Spark, Airflow, Ray, SageMaker, Vertex, etc.).
• Demonstrated ability to translate complex analytical findings into clear business recommendations and influence cross-functional decision-making.
• Experience working with ill-defined problems and driving clarity on problem definition, success metrics, and realistic tradeoffs.
• High data-quality bar: disciplined approach to validation, bias analysis, and making decisions rooted in evidence vs intuition.
• Effective communicator — able to tell the story behind the model to both highly technical and non-technical audiences.
• 8+ years of experience in data science, product analytics, or a similar quantitative role.

Our compensation plan consists of a base salary, Opendoor equity and a comprehensive package of benefits including paid time off, paid holidays, medical/dental/vision insurance, basic life insurance, and 401(k) to eligible employees.  All compensation parameters are based on experience.

#LI-RO

#LI-KC1

Data Scientist, Pricing

Location: Hyrbrid 4 onsite, 1 remote Seattle WA. 

Opendoor is transforming one of the largest, most complex markets in the world — residential real estate — using data at massive scale. Every pricing signal we generate directly impacts how we value homes, how we manage risk, and how efficiently capital moves through our marketplace. The work is highly leveraged: the quality of our pricing decisions influences conversion, margins, customer trust, and the company’s financial performance.

We are looking for mid to senior level Data Scientists. In this role, you will be a core driver of how Opendoor prices real estate at scale. You’ll operate at the intersection of economics, machine learning, experimentation, and product strategy — tackling ambiguity, shaping the pricing roadmap, and building models/analyses that materially move the business. Your insights will influence how we evaluate millions of dollars of housing inventory — and directly shape outcomes for our customers, our balance sheet, and the health of our marketplace.

What You’ll Do

• Build and maintain pricing metrics, dashboards, and frameworks.
• Run experiments and causal analyses to measure impact and drive decisions.
• Develop predictive + statistical models that improve pricing accuracy.
• Partner closely with Product, Engineering, and Operations teams to influence roadmap and model deployment.
• Deliver insights and narratives that inform executive strategy.

Skills & Qualifications

• Deep statistical reasoning: hypothesis design, experimental design, causal inference, and ability to distinguish signal vs noise.
• Proven end-to-end ML ownership: data acquisition, feature engineering, model development, validation, deployment, and ongoing monitoring.
• Strong SQL + Python proficiency; comfortable working with production data pipelines and modern ML tooling (e.g., Spark, Airflow, Ray, SageMaker, Vertex, etc.).
• Demonstrated ability to translate complex analytical findings into clear business recommendations and influence cross-functional decision-making.
• Experience working with ill-defined problems and driving clarity on problem definition, success metrics, and realistic tradeoffs.
• High data-quality bar: disciplined approach to validation, bias analysis, and making decisions rooted in evidence vs intuition.
• Effective communicator — able to tell the story behind the model to both highly technical and non-technical audiences.
• 8+ years of experience in data science, product analytics, or a similar quantitative role.

Base salary range for this role varies. Generally, the base salary range is $186,000 – $256,000 annually + RSUs + ESPP + additional employee benefits (medical/dental/vision, life insurance, unlimited PTO, 401K).

#LI-RO

#

#LI-KC1

About Us

OpenFX is on a mission to move money as freely as data, unrestricted by time zones, banking hours, or legacy systems. We are building the infrastructure that will power the next generation of cross-border payment systems for institutions. The team's execution has been exceptional, and we're scaling at a remarkable pace. Our stellar early team comes with experience in companies like J.P. Morgan, Goldman Sachs, FalconX, Paypal, Affirm, Polygon, Kraken, Nium & others. We’re backed by Accel, Lightspeed, NfX and other top-tier investors.

Role Overview

We are seeking a talented Analytics Lead to build and lead our analytics function from the ground up. This role is crucial for establishing our core data infrastructure and delivering actionable insights across all teams to power strategic decision-making and drive business growth.

Key Responsibilities:

• Data Infrastructure & Governance:
  
  
• Design and implement our core analytics infrastructure, including data warehousing, ETL processes, and visualization tools.
• Establish data quality standards, automate routine reporting, and create self-service analytics tools to ensure data accuracy and accessibility.

• Business Intelligence & Insights:
  
  
• Create and maintain dashboards for key business metrics across operations, finance, partnerships, and growth teams.
• Develop comprehensive analytics for transaction monitoring, liquidity optimization, platform performance, and revenue forecasting.
• Analyze user behavior and transaction patterns to drive growth strategies.

• Risk & Financial Analytics:
  
  
• Build analytics models for pricing optimization and financial planning.
• Create risk monitoring systems and fraud detection analytics to ensure platform security.

• Strategic Partnership & Team Leadership:
  
  
• Partner with leadership to provide data-driven insights for strategic decision-making.
• Build, manage, and mentor a high-performing team of analysts as the organization scales.

What we are looking for

Must-haves:

• Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field.
• 6+ years of experience in analytics or data science roles.
• Strong programming skills in Python and SQL.
• Experience with modern data stack tools (e.g., Snowflake, dbt, Airflow).
• Proven experience building and maintaining large-scale data pipelines.
• Excellent communication skills with the ability to present complex data to non-technical stakeholders.
• Strong problem-solving skills and meticulous attention to detail.

What helps you stand out:

• A Master's degree in a relevant field.
• Experience in a fintech or financial services environment.
• Direct experience with crypto/blockchain analytics.
• Startup experience is highly preferred.
• Expertise with data visualization tools like Tableau or Looker.

What We Offer

• Competitive salary and benefits package.
• Equity in a rapidly growing company.
• Opportunity to work in a fast-paced startup at the forefront of fintech innovation.
• Opportunity to make a significant impact on global financial infrastructure
• Collaborative work culture with emphasis on personal and professional growth.

We are committed to building a diverse and inclusive workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status. 

About Us

Nu is one of the largest digital financial platforms in the world, with more than 127 million customers across Brazil, Mexico, and Colombia. Guided by our mission to fight complexity and empower people, we are redefining financial services in Latin America and this is still just the beginning of the purple future we're building.

Listed on the New York Stock Exchange (NYSE: NU), we combine proprietary technology, data intelligence, and an efficient operating model to deliver financial products that are simple, accessible, and human.

Our impact has been recognized by global rankings such as Time 100 Companies, Fast Company's Most Innovative Companies, and Forbes World's Best Bank. Visit our institutional page [Careers at Nu - Join our team!](https://international.nubank.com.br/careers/)

About the Role

At Nu, data is the foundation that powers our AI and machine learning models, enabling millions of customers to access fair financial products. As a Machine Learning Engineer in AI Core, Data Intelligence, you’ll work across a broad spectrum — from building scalable data infrastructure and feature pipelines that feed our state-of-the-art foundation models to designing, training, and shipping transaction classification models that power critical customer experiences across the company.

You'll work at the intersection of data and applied machine learning, contributing across multiple stages of the ML lifecycle: ingesting and labeling data, training and evaluating models, and helping with deployment and production monitoring through robust quality controls. You’ll partner closely with product, compliance, and ML teams to ensure models are auditable, privacy-aware, and deliver measurable business value.

You'll join a team that manages the data engineering backbone of AI Core, ensuring data is accessible, healthy, and properly tracked across our entire ML ecosystem. Here, you'll combine your expertise in building scalable data systems with your passion for machine learning, creating solutions that enable our models to learn from better, richer data.

You can read more about the work in the AI Core team on our blog: https://building.nubank.com/understanding-our-customers-finances-through-foundation-models/

Key Responsibilities

As a Lead Machine Learning Engineer in AI Core Data Intelligence, you will:

• Design and build scalable data ingestion pipelines that bring new datasets into our AI Core platform, ensuring reliable, efficient data flow from source to model training.
• Implement data quality monitoring and validation systems that catch issues before they impact model performance, maintaining the health of datasets across our ML ecosystem.
• Model new types of data into our foundation models.
• Analyze the impact of new data sources on existing models, conducting experiments to measure performance improvements and guide data acquisition decisions.
• Develop and maintain data preparation workflows that transform raw data into features ready for model training, working with distributed computing frameworks like Ray.
• Tune and optimize machine learning models when new datasets are integrated, applying hyperparameter optimization and evaluating model performance improvements.
• Collaborate with AI Core ML, Platform, and Infra teams to ensure seamless data flow across our ML infrastructure, from ingestion to model deployment.
• Lead technical initiatives that improve our data engineering practices, setting standards for data quality, pipeline reliability, and model-data integration.
• Mentor team members and contribute to hiring activities, helping build a strong and diverse team that drives innovation in AI infrastructure.

Basic Qualifications

• Typically 6+ years of experience in machine learning engineering, data engineering, or related fields with a strong track record of building production data and ML systems.
• Proven experience designing and building data ingestion pipelines at scale, with expertise in distributed computing frameworks (Ray, Spark, or similar).
• Strong background in applied machine learning, including model training, hyperparameter tuning, and performance evaluation.
• Experience analyzing how data changes impact model performance, with the ability to design and run experiments to measure improvements.
• Proficiency in Python for data engineering and ML workflows, with experience working with large-scale data processing systems.
• Solid understanding of data quality principles and experience implementing monitoring, validation, and alerting systems.
• Strong problem-solving skills with the ability to address complex, ambiguous problems requiring coordination across multiple teams.
• Excellent communication skills, capable of explaining technical concepts to both technical and non-technical stakeholders.
• Demonstrated leadership experience, including mentoring team members and contributing to technical decision-making.

Preferred Qualifications

• Experience with MLflow or similar model tracking and versioning systems.
• Knowledge of foundation models, fine-tuning workflows, and transformer architectures.
• Experience with data pipeline orchestration tools (Dagster, Airflow, or similar).
• Background in financial services or fintech, understanding the unique data challenges in this domain.
• Experience working in a fast-paced, high-growth environment with distributed teams.
• Track record of reducing complexity in data systems and improving developer experience for ML teams.
  
  

Our Benefits

• Opportunity of earning equity at Nu
• Medical Insurance
• Dental and Vision Insurance
• Life Insurance and AD&D
• Extended maternity and paternity leaves 
• Nucleo - Our learning platform of courses
• NuLanguage - Our language learning program
• NuCare - Our mental health and wellness assistance program
• Extended maternity and paternity leaves 
• 401K
• Saving Plans - Health Saving Account and Flexible Spending Account
• Work-from-home Allowance
• Relocation Assistance Package, if applicable.

Work Model for this Role

Hybrid 2-3 times/week: Our hybrid work model brings us to the office at least twice a week, on strategic days designed to maximize team connection and collaboration. For more details, visit https://building.nubank.com/nu-hybrid-work-model/

Locations: This role is available in any of our North American offices (Palo Alto, USA; Miami, USA; Durham, USA; Toronto, CAN)