Deployment Strategist

Location

New York, NY (Union Square) or San Francisco, CA (SoMa)

Hybrid, with 3–4 days in the office per week and approximately 10–20% domestic travel for customer deployments.

Company Stage of Funding

Series D

Valuation: $2.1B

Total Funding: $230M+

Office Type

Hybrid. Candidates can work from the New York City or San Francisco office. The team generally works in the office Tuesday through Thursday, with a minimum of two in-office days per week. Domestic travel is required to work directly with enterprise customers and support live deployments.

Salary

$140,000–$200,000 base salary

The broader hiring overview references compensation up to approximately $240,000–$248,000; the listed role range is $140,000–$200,000.

Equity

Competitive equity compensation through RSUs or stock options, depending on eligibility and offer structure. Equity is subject to the applicable vesting schedule, with a one-year cliff followed by monthly vesting.

Sign-on bonuses may be available.

Visa

Open to visa transfers, including OPT and H-1B transfers.

Employment Type

Full-time

Company Description

This company is transforming the mortgage servicing industry by building an AI-native software platform that automates complex operational workflows for mortgage servicers.

Its platform, ValonOS, is designed to modernize mortgage servicing by replacing fragmented legacy processes with scalable software, automation, and AI-powered workflows. The company is transitioning from a mortgage servicing business toward a software platform serving large enterprise customers in the US housing finance industry.

The company has raised more than $230M in funding and is valued at approximately $2.1B following its Series D. It is backed by prominent investors, including Andreessen Horowitz and WestCap, and has approximately $500M in signed contracts. Its long-term ambition is to power a substantial share of US home loan servicing through its software platform.

The company operates in a complex, highly regulated industry where enterprise deployments require a deep understanding of customer operations, data, technology, and business processes. Implementing the platform involves working directly with large mortgage servicers, understanding their existing workflows, configuring software, driving adoption, and ensuring deployments produce measurable business outcomes.

The Deployment Strategist role sits at the intersection of strategy, operations, technology, and customer delivery. It is designed for high-agency individuals who want to move beyond making recommendations and take direct ownership of execution and results.

The team is looking for candidates from top strategy consulting firms, investment banks, private equity, hedge funds, or high-growth startups who can solve ambiguous problems, work with data, use AI tools effectively, and build scalable processes from scratch.

The culture emphasizes first-principles thinking, ownership, rapid execution, AI-native workflows, and the ability to make sound decisions with incomplete information. Deployment Strategists work closely with engineering, product, and customer stakeholders to turn complex legacy operations into scalable software-driven workflows.

What You Will Do

1. Own Enterprise Deployments End to End

• Lead live deployments of an enterprise mortgage servicing platform for large financial institutions.

• Understand how each customer's existing operations, workflows, and systems function.

• Translate business requirements into platform configurations and implementation plans.

• Coordinate deployment activities from initial discovery through implementation, adoption, and ongoing improvement.

• Take ownership of measurable customer outcomes rather than stopping at recommendations or project plans.

• Ensure deployments solve real operational problems and deliver tangible business value.

2. Understand Customer Operations and Workflows

• Spend time on-site with customers to observe workflows, understand operational challenges, and identify inefficiencies.

• Analyze customer processes, data, and legacy systems to determine what is actually happening in practice.

• Work directly with customer stakeholders to uncover requirements that may not be clearly documented.

• Identify opportunities to simplify processes, eliminate manual work, and improve operational performance.

• Navigate different customer environments and adapt implementation approaches to their specific needs.

3. Translate Business Problems Into Technical Solutions

• Partner with Product and Engineering to translate complex operational requirements into scalable software solutions.

• Help determine how customer workflows should be represented and implemented within the platform.

• Use data analysis and basic technical tools to investigate problems and validate assumptions.

• Collaborate with technical teams to identify implementation constraints and design practical solutions.

• Bridge the gap between business stakeholders and software engineers, ensuring that technical work addresses the underlying business problem.

4. Make Decisions in Ambiguous Environments

• Work through unfamiliar and complex problems without relying on fully defined requirements.

• Apply first-principles reasoning to separate facts from assumptions and identify the root cause of issues.

• Make sound decisions with incomplete information and adapt as new evidence emerges.

• Challenge existing processes and assumptions when they prevent effective implementation.

• Balance immediate deployment needs with long-term scalability and customer outcomes.

5. Build Playbooks, Tooling, and Processes

• Develop repeatable playbooks and implementation frameworks for future enterprise deployments.

• Identify patterns across customer implementations and turn them into standardized processes.

• Build or define tooling that improves deployment speed, consistency, and operational efficiency.

• Use AI tools and coding agents to automate repetitive tasks and improve execution.

• Help the organization scale its deployment function as the number and complexity of enterprise customers grow.

6. Drive Customer Adoption and Business Results

• Ensure customers understand and adopt the platform effectively.

• Monitor implementation progress and identify obstacles to successful adoption.

• Work with customer stakeholders to resolve operational and technical challenges.

• Evaluate whether deployments are delivering the intended business outcomes.

• Take responsibility for the quality and success of the overall implementation.

7. Use AI and Data to Improve Execution

• Actively use AI tools and coding agents in day-to-day work.

• Identify opportunities to automate manual analysis, documentation, research, and operational processes.

• Use SQL, Python, and other data tools where appropriate to investigate workflows and solve problems.

• Develop AI-enabled workflows that make deployments more efficient and repeatable.

• Experiment with new tools and methods to improve productivity without compromising accuracy or customer outcomes.

8. Collaborate Across Teams

• Work closely with Engineering, Product, and customer-facing stakeholders.

• Translate customer feedback into actionable technical and operational requirements.

• Communicate deployment progress, risks, and decisions clearly.

• Align internal teams around priorities and implementation plans.

• Contribute to a culture of ownership, fast execution, and continuous improvement.

Ideal Candidate Background

Experience Requirements

• 3–6 years of professional experience.

• Background in management consulting, investment banking, private equity, hedge funds, or a high-growth technology startup.

• Experience at a top consulting firm, top investment bank, or top private equity/hedge fund is a dealbreaker requirement.

• Demonstrated ability to solve complex business problems and execute under pressure.

• Experience working on ambiguous, high-impact projects with significant ownership.

• Evidence of building processes, playbooks, tooling, or operating models from scratch.

• Strong track record of professional stability; multiple tenures of less than one year are a concern.

• Promotions and increasing responsibility within the same organization are strong positive signals.

Education Requirements

• Undergraduate degree from a top 20 university or a recognized international equivalent.

• Relevant international institutions may include Oxford, Cambridge, LSE, and Waterloo.

• Strong academic foundation and evidence of analytical rigor.

• Education should be evaluated alongside professional achievement, problem-solving ability, and demonstrated ownership.

Consulting, Finance, and Startup Experience

• Experience at a leading strategy consulting firm, such as McKinsey, Bain, or BCG.

• Alternatively, experience at a top investment bank such as Goldman Sachs, JPMorgan, Morgan Stanley, Lazard, or Evercore.

• Experience at a top private equity firm or hedge fund is also relevant.

• High-growth startup experience, particularly at companies such as Scale AI or similar organizations, is a strong signal.

• Experience translating complex analysis into practical execution.

• Ability to manage competing priorities, work with senior stakeholders, and deliver under demanding timelines.

AI Fluency and Technical Skills

• Demonstrated, active use of AI tools and coding agents in current workflows.

• Ability to identify practical applications of AI for analysis, automation, and operational execution.

• Willingness to learn and apply new technical tools quickly.

• Basic SQL and Python knowledge is preferred.

• Comfort working with data, investigating operational problems, and validating assumptions.

• Ability to collaborate effectively with software engineers and product teams.

• Technical depth does not need to match that of a software engineer, but practical AI fluency is a non-negotiable requirement.

Problem-Solving and Analytical Ability

• Strong first-principles thinking and structured problem-solving.

• Ability to break down ambiguous problems into actionable steps.

• Comfort making decisions with incomplete information.

• Ability to distinguish between symptoms and root causes.

• Strong quantitative and analytical reasoning.

• Ability to evaluate alternatives and prioritize based on business impact.

Ownership and Execution

• High agency and a strong bias toward action.

• Comfortable moving from recommendations to direct execution.

• Demonstrated ownership of projects and measurable outcomes.

• Able to build processes, tooling, and playbooks without relying on existing templates.

• Proactively identifies problems and develops practical solutions.

• Comfortable operating in a fast-moving environment with limited structure.

• Able to manage several stakeholders and implementation priorities simultaneously.

Communication and Stakeholder Management

• Strong written and verbal communication.

• Comfortable working with enterprise customers and senior stakeholders.

• Able to translate between business requirements and technical implementation.

• Can explain complex ideas clearly and concisely.

• Works effectively across Product, Engineering, and operational teams.

• Builds trust through reliability, sound judgment, and consistent execution.

Nice-to-Have Qualifications

• Experience at Scale AI or a similar high-growth AI-native startup.

• Experience deploying enterprise software or implementing complex technology solutions.

• Experience working with mortgage servicing, financial services, or regulated enterprise environments.

• Familiarity with SQL, Python, data analysis, and data warehousing.

• Experience building operational playbooks, internal tooling, or scalable implementation processes.

• Experience using AI coding agents to automate workflows or accelerate execution.

• Experience managing enterprise implementations or complex cross-functional projects.

• Experience in roles that combine strategy, operations, and technology.

Compensation & Benefits

• Base salary range of $140,000–$200,000 for the listed role.

• Competitive equity through RSUs or stock options, depending on the offer.

• Sign-on bonuses may be available.

• Hybrid working arrangements in New York City or San Francisco.

• Relocation assistance of up to $10,000, subject to eligibility.

• Lunch and dinner provided on in-office days.

• Dog-friendly New York City office.

• Opportunity to work on complex enterprise software deployments in the mortgage servicing industry.

• High ownership and direct exposure to enterprise customers, engineering, and product strategy.

• AI-native work environment with coding agents and custom AI workflows integrated into daily operations.

Why Join

• Help modernize a large, complex, and highly consequential financial services industry.

• Own enterprise deployments from initial discovery through implementation and measurable business results.

• Work on eight- and nine-figure enterprise software contracts and complex customer environments.

• Move beyond strategy and analysis into direct ownership of execution and outcomes.

• Use AI tools and coding agents to automate work and improve operational efficiency.

• Build playbooks, tooling, and processes that enable the company to scale deployments.

• Collaborate directly with Engineering and Product to translate real-world problems into software solutions.

• Join a well-funded, high-growth Series D company valued at approximately $2.1B.

• Help scale a software platform with substantial signed contracts and ambitious growth plans.