Founding Data Scientist

Quant Link is a financial technology company building data and analytics tools for public equity markets. We use modern software engineering, machine learning, and quantitative research to help investors better understand how companies and stocks have behaved historically under different conditions.

Our platform focuses on:

• Organizing and analyzing large sets of technical, fundamental, and price-based data
• Providing transparent, evidence-based analytics and tools
• Supporting investors and professionals in making more informed, data-driven decisions

As an early-stage company, we work in a focused, collaborative environment where people can take real ownership of their projects and see their work used in production.

We are looking for a Founding Data Scientist to be one of the earliest technical hires on the team. This is a full-time role based in Minneapolis, MN
, with a hybrid work arrangement (a mix of in-office and remote work). You will lead the design, development, and deployment of the core models and analytics that power Quant Link. You’ll work closely with the founders, engineering, and product to define our data science roadmap, evaluate new ideas, and translate research into reliable, production-ready tools.

This role is a good fit for someone who enjoys owning problems end-to-end, from framing the question and exploring data, to building models, shipping them to production, and measuring their impact.

• Lead the design and implementation of statistical and machine learning models on large, noisy financial and alternative datasets
• Develop and evaluate predictive and descriptive models related to stock behavior and risk/return characteristics
• Design, implement, and maintain feature pipelines using technical, fundamental, and price-based data
• Build clear, reliable visualizations, dashboards, and analytical tools to communicate insights internally and to end users
• Design and run structured evaluations (e.g., backtests, A/B tests, and other experiments) to assess model performance and robustness over time
• Work with engineering to bring models into production, including monitoring, diagnostics, and iteration
• Partner with product and founders to identify high-impact questions, prioritize work, and shape the overall data science roadmap
• Establish best practices for data quality, documentation, experiment design, and reproducibility
• Mentor future data science hires as the team grows and contribute to building a strong technical culture

• 4+ years of experience in Data Science, Machine Learning, or Quantitative Research
  , including building models that have been used in real products or decisions
• Strong proficiency in Statistics
  , including:
  • Regression and generalized linear models
  • Distributions, uncertainty, and inference
  • Hypothesis testing and experiment design
• Advanced analytical and problem-solving skills
  , with a track record of framing ambiguous problems and driving them to conclusions
• Proficiency in Python (e.g., pandas, Num Py, scikit-learn or similar) and experience writing production-quality code
• Working knowledge of SQL and experience working with relational databases and large datasets
• Practical experience with machine learning techniques
  , such as:
  • Linear and logistic regression
  • Tree-based models and ensembles
  • Clustering and dimensionality reduction
  • Time-series modeling concepts
• Experience building and using data visualization and reporting tools (e.g., Matplotlib, Plotly, Tableau, Power BI, or similar)
• Experience taking models or data products from prototype to production in partnership with engineers
• Ability to work collaboratively in a small, fast-paced team and to communicate clearly with both technical and non-technical stakeholders
• Comfort working in an early-stage startup environment with evolving priorities and a high degree of ownership

• Experience with financial markets, equity research, portfolio analytics, or trading systems
• Hands-on experience with time-series modeling
  , including forecasting and evaluation in live or semi-live settings
• Experience with cloud platforms (AWS, GCP, or Azure) and modern data tooling (e.g., dbt, Airflow, Spark,…