Cloud Capital is a pioneering force at the forefront of the cloud efficiency space. We help growing technology companies slash their cloud infrastructure costs. Our cutting-edge SaaS platform offers powerful forecasting and planning capabilities, enabling our clients to make smarter, data-driven decisions. Using innovative software, we maximize customer cost savings while minimizing cloud commitment risks, ensuring our clients can scale their businesses without financial constraints.
This is an opportunity to join a highly technical, fast-moving team focused on building the forecasting, decision support, and optimization models that power our product. You’ll research and implement novel approaches, contribute statistical insight to customer decisions, and translate ambiguity into prototypes that clarify and inspire.
You’ll be joining a small, experienced team of full stack data-science engineers with strong ML backgrounds. Your role will be to bring additional statistical depth and a causal modeling mindset to an already rigorous team. Whether you're implementing techniques from the latest ML papers, building interpretable models for CFOs, or pressure-testing forecasting approaches against messy cloud data, you’ll have room to do deeply technical work that matters.
The existing team has developed models to calculate the risk-costs associated with cloud commitments and their uncertain and variable consumption. The ideal candidate will bring with them a background in financial disciplines and an ability to critique and enhance these models with learnings from areas like options pricing, insurance risk modeling, and hedge fund strategies.
Model Prototyping & Implementation
Research, evaluate, and implement statistical and ML models for forecasting and decision making
Translate papers and innovative ideas into working prototypes and evaluations
Build tools to validate model performance and stress-test assumptions
Own the full data-science lifecycle: pitch, prototype, evaluate, and deploy
Statistical Thinking
Apply hypothesis testing, causal inference, and other statistical frameworks to evaluate product and customer hypotheses
Work with noisy real-world data to extract signal to facilitate meaningful decision making
Quantify uncertainty and build trustable metrics for both internal and external users
Engineering & Product Collaboration
Collaborate with frontend engineers to help our product tell our users the data-stories your models create.
Write maintainable, well-tested Python code and contribute to modeling libraries
Build new AI tools to accelerate your own workflow and enhance the capabilities of our users
User-Centered Problem Solving
Reframe technical challenges through the lens of user value
Ask and answer “So what?” to distill insights into ingredients that help our customers make decisions
Empathize with customer and stakeholder needs to uncover valuable, practical solutions
Experience
6+ years of experience in data science, ML engineering, or applied statistics
Demonstrated ability to implement and adapt algorithms from research
Deep understanding of statistical modeling, causal inference, hypothesis testing, and experimental design
Proficient in Python and scientific libraries like NumPy, pandas, scikit-learn, PyMC, or similar
Familiarity with time series modeling, regression, Bayesian inference, and deep learning
Background in financial modeling, risk pricing, or quantitative finance disciplines is a strong plus
Skills & Traits
Inventor’s mindset—you constantly look for ways to build faster, better, cheaper systems
Curious and rigorous; you want to understand why something works, not just that it does
Strong communicator; you are able to explain complex ideas to technical and non-technical teammates
Able to empathize with customers and translate their needs into modeling strategy
Comfortable with ambiguity and eager to explore multiple solution paths
Fast prototype-to-insight loop: you move quickly and share what you learn
Tenacious for progress; expect regular virtual-watercooler conversations with your team-mates about their latest swings at AGI
Bonus
Experience reading and reproducing academic ML or stats papers
Exposure to cloud infrastructure datasets (AWS, GCP, etc.) or forecasting business systems
Familiarity with modern deployment environments (e.g., AWS Lambda, GitHub Actions, Docker, Terraform)
Data: Python, Pandas, Scikit-learn, Tensorflow, and DuckDB
Backend: AWS Lambda and Fargate, Node.js, Postgres
Infrastructure: Fully serverless, AWS-native
Tooling: Claude-Code, Cursor, Multiple Custom MCP implementations. Anything to maximize human leverage on interesting problems while outsourcing the boring problems.
This is an opportunity to join a well-funded startup operating in a massive, fast-growing market. Join us on our mission to optimize cloud spending across the board, providing CFOs with crystal-clear visibility into cloud spending, freeing engineers from the burdens of cost commitments, and leveraging financial markets to mitigate risk for all stakeholders. Our unique and innovative approach ensures that our customers can achieve efficient cloud spend in harmony with cloud providers' capabilities and offerings.
Cloud Capital is committed to building a diverse and inclusive team. We welcome applicants from all backgrounds, experiences, and perspectives. If you are unsure you meet every requirement, please apply - we value potential and growth.
Job details are sourced from the employer's original posting.
Open job postingAbout the company
Cloud Capital provides financial services and investment solutions leveraging cloud technology.