Kiavi is one of the nation’s largest private lenders to residential real estate investors (REIs). We harness the power of data & technology to offer our customers a simpler, more reliable, and faster way to access the capital they need to scale their businesses. Formerly known as LendingHome, we’re committed to helping REIs revitalize the approximately $25 trillion worth of aged U.S. housing stock to provide move-in ready homes and rental housing for millions of Americans across the country. Combining our technology and industry expertise, Kiavi has grown to a team of over 500 and has won many awards over the years including Forbes Fintech 50, Finovate Awards, Best Digital Mortgage Platform, Fast Company’s Most Innovative Companies, and many more. - Loans Funded: $30 billion in loans funded - Homes financed: 100,000+ projects funded for our customers across the country - Geography: 49 states + D.C. where we lend to REIs so they can revitalize neighborhoods - Employees: Over 500 employees with competitive benefits and perks This position can be based remotely in any of our approved hiring regions. Our hybrid distributed workforce philosophy allows us to find the best talent in the country and build diverse teams, while allowing our colleagues to work from the location that works best for them. 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. THE TEAM Our Risk 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 Risk Analytics team is seeking a talented Data Scientist to join us in our effort to reinvent real estate investment lending. You will be working with the Director of Risk Analytics to tackle fun business problems related to credit risk management, asset valuation, process optimization, and more. An ideal candidate should be able to utilize advanced data manipulation and modeling techniques to work on structured and unstructured data to solve complex business problems. You’ll also have interaction with MLOps engineers, Product Managers, and Capital Markets subject matter experts to help drive select initiatives. If you are keen on joining a dynamic company and working on intellectually challenging problems in a fast paced environment, please reach out to us. KEY RESPONSIBILITIES: - Develop and maintain innovative models for asset valuation. This requires thorough statistical analysis of the underlying data and understanding of the various macroeconomic factors and risk factors that impact prices and liquidity in the real estate market. - Develop models for portfolio management purposes such as value forecasting or market risk segmentation. - Closely provide regular ongoing model performance assessments and enhance existing models. - Develop analytical tools to support other teams - Working with the technology team, drive initiatives to enhance data capabilities and streamline infrastructure. - Work closely with engineers/data infrastructure team in designing useful data attributes. - Perform exploratory analysis and make recommendations on improving current data models, decisioning frameworks, etc. - Refactor existing analytic solutions to improve scalability. EXPERIENCE & QUALIFICATIONS: - Bachelor's degree in a quantitative discipline (e.g., statistics, economics, finance, physics, mathematics) and 2+ years of work experience in a modeling group at a commercial bank, investment bank, consulting firm, or fintech company OR Advanced degree (PhD or MS) in a quantitative discipline - Statistical skills especially in model development at an account, customer, or asset level. - Solid understanding of machine learning techniques and AI capabilities - Strong programming skills in R, SQL, Python - Team player with strong interpersonal skills - Excellent oral and written communication skills with the ability to explain technical concepts in practical terms PREFERRED REQUIREMENTS: - Hands on experience with automated valuation models - Exposure to time-to-event/survival analysis problems or financial econometric modeling - Experience in handling unstructured data - Experience with Spark - Experience with Monte Carlo simulation and/or Bayesian statistics - Progress towards FRM or CFA certification Kiavi takes a market-based approach to pay, and pay may vary depending on your location. Work locations are categorized into one of three tiers based on cost of labor for that geographic area. These ranges may be modified in the future. Region 1 - $154,500 - $231,700 + equity + benefits Region 2 - $146,700 - $220,100 + equity + benefits Region 3 - $139,000 - $208,400 + equity + benefits To find a location’s tier, please refer to https://kiavi.com/compensation. 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 benefits. Benefits and Perks - Medical, Dental, and Vision Insurance: Kiavi offers free employee-only coverage for medical, dental, and vision plans. Dependents receive a heavily discounted premium, competitively placed in the 90th percentile for cost. - Health Savings Account (HSA): For those enrolled in a High Deductible Health Plan (HDHP), Kiavi provides an annual employer contribution to HSAs: $1,000 for individual coverage and $2,000 for dependents. - Disability and Life Insurance: Immediate access to short-term and long-term disability insurance, as well as group life and AD&D insurance, with coverage at 1x annual salary. - Remote Work Policy: Kiavi supports working remotely for day to day activities for most roles and occasionally may ask employees to gather in person for key meetings, training, team building etc. For remote workers, Kiavi offers a one-time office setup reimbursement of up to $500 and a monthly $85 stipend for internet and phone expenses. For in-office workers, Kiavi offers employer contributions to commuter benefits account, either for parking or public transportation. - Flexible Time Off: Employees are empowered to manage their own time off, with no strict limits, as long as it's approved by their manager. - Paid Leaves: Up to 8 weeks of paid leave for medical or caregiver purposes, and 12 weeks of paid maternity/paternity leave. Short-term disability leave is also available for birthing mothers. - 401(k) & Company Match: A 401(k) plan managed by Fidelity, with Kiavi matching 100% of contributions up to 3% of compensation or $8,000 (whichever is less). All employer contributions are immediately vested. Protect yourself from recruitment scams: - If you are offered an interview or a role with Kiavi, this will be communicated with you through email, via an official email address ending in @kiavi.com or @lendinghome.com. - You will never be asked for payments of any kind during the process. - We also never communicate with candidates via Whatsapp at any point during the recruitment process. - We won’t ask for personal information or data via text message. If you have any concerns regarding how genuine a text message is, please contact your recruiter. - You can find our official Kiavi careers page here. We advise you to check that the page details and website addresses match if you have any concerns. Kiavi is an Equal Opportunity Employer San Francisco Fair Chance Ordinance Police Code, Article 49 We use E-Verify to confirm the identity and employment eligibility of all new hires.

• Lead the analytics support for the Supplier Quality organization. • Establish the process that takes new ideas through implementation and maintenance. • Partner with senior leaders across organizations to drive user adoption of existing suite of tools. • Build the strategy around usage of predictive analytics for business objectives. • Incorporate predictive capabilities into existing tool suite and build new tools with larger business impact. • Partner across DA&DI and IT teams to support an integrated full value-stream data pipeline for machine learning capabilities.

• Develop and Deploy Production-Grade AI Systems • Build, deploy, and maintain scalable APIs that serve AI/ML models in production environments • Own end-to-end delivery of AI solutions, from prototyping to fully productionized systems • Design and implement Retrieval-Augmented Generation (RAG) systems using vector databases • Build and orchestrate AI agents using frameworks such as LangGraph, CrewAI, or similar • Evaluate and select appropriate large language models (LLMs) and foundation models based on specific use cases • Continuously optimize model inference for latency, cost efficiency, and throughput at scale • Identify bottlenecks and implement improvements to ensure high-performing systems • Implement robust monitoring, logging, and alerting for deployed models and services • Ensure system reliability, uptime, and performance through best practices in production ML systems • Build and maintain CI/CD pipelines for seamless testing, deployment, and iteration of AI systems • Contribute to evolving engineering standards as the company transitions from rapid experimentation to more structured, scalable operations • Work closely with product and engineering teams to integrate AI capabilities into core products • Translate business problems into technical solutions and clearly communicate trade-offs • Help bring stability and structure to AI development processes during a high-growth, scale-up phase • Balance speed of execution with the need for maintainable, well-documented, and scalable systems • Take ownership of ambiguous problems and drive them through to practical, production-ready outcomes

Role Description Como Cientista de Dados, você desempenhará um papel essencial na análise de dados e no desenvolvimento de modelos de Machine Learning para extrair informações valiosas a partir de nossos conjuntos de dados. Sua experiência será fundamental para aprimorar nossas estratégias de negócio e melhor atender nossos clientes. O trabalho é importante para embasar tomadas de decisão, combinando conhecimentos em estatística, matemática e programação, dentro da área financeira com Open Finance. - Construir modelos de machine learning; - Analisar dados e desenvolver algoritmos de text mining para extrair informações; - Aplicar técnicas de regressão para modelar relacionamentos complexos em dados; - Avaliar modelos de dados garantindo precisão e eficácia; - Estruturar e analisar dados para geração de insights de negócio. Qualifications - Experiência em construção de modelos de machine learning, text mining e regressão; - Exploração e preparação de dados para extrair insights de negócio; - Habilidades em análise estatística e análise de dados; - Experiência plena com Python, R, PySpark, Spark, SQL, Bigdata e em ambientes Hadoop; - Experiências com Azure, Databricks, Databricks GenAI. Requirements - Triagem de currículos; - Bate-papo com equipe de Gestão Humana; - Conversa com o cliente; - Entrevista Técnica com gestor(a) da área. Benefits - Vales alimentação/refeição - no cartão de benefícios flexíveis Swile; - Cobertura de até 100% em Plano de Saúde e Odontológico; - Seguro de Vida em grupo; - Trabalho remoto ou híbrido; - Convênio Saúde Mental - psicoterapia online e presencial; - Incentivo a certificações e cursos; - Convênio para cursos de pós-graduação e MBA; - Parceria com escolas de idiomas; - Convênio com academias, estúdios e apps de bem-estar (Gympass); - Palestras e rodas de conversa internas; - Bônus por indicação; - Happy hours; - Mimos em datas comemorativas.