Senior Applied Scientist - AVM & ML Leadership Role

Kiavi; United States; Remoto; USD 175.000 - 225.000; Tempo pieno

Descrizione del lavoro

Kiavi is seeking a Senior Applied Scientist to lead end-to-end development of automated valuation models (AVMs) and ML systems for real estate lending. This senior IC/technical leader role emphasizes production ML, complex modeling, and cross-functional collaboration with engineering, Product, Credit Risk, and Capital Markets.

You will mentor colleagues, drive strategic ML initiatives from research to production, and shape data architectures for scalable decisioning in a fully remote, US-based

Competenze

• Ph.D. or Master's degree in a quantitative discipline with 5+ years of industry experience building and deploying ML models in production.
• Proven track record of developing statistical and machine learning models at an account, customer, or real estate asset level.
• Strong production-level programming in Python, SQL, and R.
• Demonstrated experience in technical leadership, cross-functional collaboration, and mentoring junior team members.
• Excellent oral and written communication skills with the ability to explain complex technical concepts to non-technical stakeholders.

Mansioni

• Lead the end-to-end design, architecture, development, and deployment of AVM systems for novel lending products.
• Architect and refine ML, deep learning, and statistical models for asset valuation, incorporating spatial-temporal trends, macroeconomic factors, market liquidity, and property risk dynamics.
• Establish robust quantitative frameworks for continuous model performance assessment, monitoring, and automated retraining across changing real estate cycles.
• Build analytical tools and frameworks to empower cross-functional teams across risk, underwriting, and capital markets.
• Partner closely with data infrastructure and MLOps engineering teams to drive initiatives that enhance data capabilities, build feature stores, and streamline real-time inference infrastructure.
• Design useful data attributes and feature representations, leveraging structured, unstructured, and multimodal data sources (e.g., property photos, listing descriptions, spatial features).
• Perform exploratory data analysis, evaluate frontier AI capabilities, and make strategic recommendations to improve current decisioning frameworks and valuation accuracy.
• Refactor and optimize existing analytical solutions and algorithmic components to improve scalability, reliability, and execution efficiency.

Conoscenze

Python

SQL

R

Leadership

Mentoring

Communication

Formazione

Master's or Ph.D. in a quantitative field

Strumenti

Spark

PyTorch

TensorFlow

AWS/GCP

Descrizione del lavoro

Senior Applied Scientist - AVM & ML Leadership Role — Remote

This is a senior individual contributor / technical lead role at Kiavi, a fintech lender focused on real estate — a relatively niche but growing sector where applied ML expertise commands a premium. The $175K–$225K base (plus bonus) is competitive but not exceptional for a Senior Applied Scientist in top-tier tech markets; comparable roles at FAANG or large hedge funds typically range $200K–$280K base. However, the domain specialization in real estate AVMs and fintech lending can accelerate career trajectory into specialized ML leadership roles.

• Production AVM systems — a rare, high-value specialization in proptech and mortgage finance


• MLOps at scale (feature stores, real-time inference, AWS/GCP)


• Cross-functional technical leadership and stakeholder communication

Green flags: Clear ownership of end-to-end ML systems, exposure to capital markets and credit risk domains, mentorship opportunities, and a PhD-friendly culture. The breadth of the stack (deep learning, Bayesian inference, causal ML) keeps skills sharp and transferable.

Yellow flags: "Fast-paced environment" and broad scope (risk, underwriting, capital markets, MLOps) could indicate resource constraints. No explicit mention of team size or engineering headcount may warrant clarification. The identity verification step (biometrics) is unusual in hiring pipelines — worth confirming the vendor.

Overall, this is a strong role for a senior ML scientist looking to build domain depth in real estate finance while maintaining broad technical skills.

Our Property Analytics team is made up of 360-degree thinkers that analyze complex problems and come up with creative solutions. We love to combine advanced analytics and machine learning skills with deep domain knowledge about the business when creating solutions. You will work with some of the most fascinating data sets in the world, alongside a world-class group of product engineers, sales and marketing leaders, and business operators.

PURPOSE OF THE ROLE

Kiavi's Property Analytics team is seeking a Senior Applied Scientist to join us in our effort to reinvent real estate lending. You will be working alongside engineering, product, and business leaders to tackle complex research and machine learning problems related to automated valuation models (AVMs), asset valuation, and process optimization.

An ideal candidate brings deep expertise in advanced machine learning, statistical modeling, and production-grade AI system architectures. You will lead technical direction, mentor team members, and partner closely with MLOps engineers, Product Managers, Credit Risk Management and Capital Markets subject matter experts to drive select strategic initiatives from initial research to production deployment.

KEY RESPONSIBILITIES:

• Lead the end-to-end design, architecture, development, and deployment of state‑of‑the‑art automated valuation modeling (AVM) systems for novel lending products.


• Architect and refine innovative machine learning, deep learning, and statistical models for asset valuation, incorporating spatial‑temporal trends, macroeconomic factors, market liquidity, and property risk dynamics.


• Establish robust quantitative frameworks for continuous model performance assessment, monitoring, and automated retraining across changing real estate cycles.


• Build analytical tools and frameworks to empower cross-functional teams across risk, underwriting, and capital markets.


• Partner closely with data infrastructure and MLOps engineering teams to drive initiatives that enhance data capabilities, build feature stores, and streamline real-time inference infrastructure.


• Design useful data attributes and feature representations, leveraging structured, unstructured, and multimodal data sources (e.g., property photos, listing descriptions, spatial features).


• Perform exploratory data analysis, evaluate frontier AI capabilities, and make strategic recommendations to improve current decisioning frameworks and valuation accuracy.


• Refactor and optimize existing analytical solutions and algorithmic components to improve scalability, reliability, and execution efficiency.

EXPERIENCE & QUALIFICATIONS:

• Ph.D. or Master's degree in a quantitative discipline (e.g., Computer Science, Data Science, Statistics, Economics, Applied Mathematics, Machine Learning) with 5+ years of industry experience (or 3+ years with a Ph.D.) building and deploying machine learning models in production.


• Proven track record of developing statistical and machine learning models at an account, customer, or real estate asset level.


• Solid understanding and hands‑on application of advanced machine learning techniques, deep learning, spatial analytics, and AI capabilities.


• Strong production‑level programming skills in Python, SQL, and R.


• Demonstrated experience in technical leadership, cross‑functional collaboration, and mentoring junior team members.


• Excellent oral and written communication skills with the ability to explain complex technical, algorithmic, and mathematical concepts clearly to non‑technical stakeholders and executive leadership.

PREFERRED REQUIREMENTS:

• Hands‑on experience developing, evaluating, and deploying Automated Valuation Models (AVMs) or automated appraisal systems.


• Exposure to time‑to‑event/survival analysis, financial econometric modeling, or spatial‑temporal forecasting.


• Experience handling and modeling unstructured data, including computer vision or natural language processing applications in real estate.


• Proficiency with distributed computing frameworks (e.g., Spark) and modern deep learning frameworks (e.g., PyTorch, TensorFlow).


• Experience with Monte Carlo simulations, Bayesian inference, or causal machine learning.


• Familiarity with MLOps best practices, cloud architecture (AWS/GCP), feature stores, and model serving infrastructure.


• Progress towards or completion of relevant quantitative/financial certifications (e.g., FRM or CFA).

Identity Verification Notice: To ensure the security and integrity of our hiring process, all candidates are required to complete a secure identity verification. This process uses biometric and device validation to confirm identity and liveness. All data is handled securely in accordance with our privacy policy.

Kiavi takes a market-based approach to pay, and pay may vary depending on your location. The expected annualized base pay range for this full-time role to be performed remotely is $175,000 – $225,000 base + bonus + benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries. Within the range, the successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. Your recruiter can share more about the specific base pay range for your preferred location during the hiring process. Please note that the pay range listed above reflects the base salary only, and does not include the target value of bonus or benefits.

We use E-Verify to confirm the identity and employment eligibility of all new hires.

Is this role truly fully remote or are there office requirements? The role is listed as fully remote, but it is restricted to candidates within the United States. Kiavi takes a market-based approach to pay, so compensation may vary slightly by location. What is the salary range for the Senior Applied Scientist position at Kiavi? The annualized base pay range is $175,000–$225,000, plus bonus and benefits. The final offer depends on skills, experience, qualifications, and work location. What level of experience is required for this role? Kiavi requires a Master's or Ph.D. in a quantitative field with at least 5 years of industry experience (or 3+ years post-Ph.D.) building and deploying production ML models. Is experience with real estate or Automated Valuation Models (AVMs) required? AVM experience is listed as preferred but not strictly required. The core requirements focus on advanced ML, statistical modeling, and production deployment experience. Does Kiavi require work authorization verification? Yes, Kiavi uses E-Verify to confirm the identity and employment eligibility of all new hires, so valid U.S. work authorization is mandatory.

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