Principal, Data Scientist (Finance)

Datasite•Minneapolis, MN

About The Position

As a Principal Data Scientist, you will be the technical lead and primary "engine" of our data science initiatives. Reporting to the Director of Data Science, you are a high-level individual contributor responsible for solving our most complex, abstract business problems through advanced mathematical modeling. You will lead the end-to-end development of "Data Intelligence Products" - from high-dimensional forecasting and financial risk engines to ML-driven revenue optimization. While this is an IC role, you will set technical standard for the department, acting as a mentor to junior analysts and an architect for our predictive ecosystem.

Requirements

8–10+ years of experience in Data Science, with a track record of delivering high-impact predictive models in a corporate or production environment.
Proven IC Leadership: Experience as a staff or principal-level contributor who has successfully led large-scale technical projects from concept to deployment.
Modern Stack Experience: Deep familiarity with the Snowflake + dbt + Power BI ecosystem is a significant advantage.
Proven track record of building and deploying predictive models using Python and SQL that achieved high business adoption.
Toolkit Expertise: Hands-on experience with ML orchestration tools and automated testing for model performance (e.g., monitoring for Data Drift and Model Decay).
Cloud Infrastructure: Experience with cloud-based ML platforms (e.g., Azure ML, AWS SageMaker, or Databricks) and how they integrate with data warehouses like Snowflake.
Advanced Education: PhD or Master’s degree in a highly quantitative discipline (e.g., Physics, Mathematics, Statistics, Computer Science, or Economics).
The ML Toolbelt: Deep expertise in the Python Data Science stack (e.g., scikit-learn, XGBoost, LightGBM) and deep learning frameworks (e.g., PyTorch or TensorFlow).
Predictive Expertise: Deep experience in time-series forecasting, supervised learning, and causal inference.
Time-Series & Forecasting: Mastery of libraries dedicated to financial and demand forecasting, such as Prophet, statsmodels, or sktime.
MLOps & Deployment: Experience with model lifecycle management tools (e.g., MLflow, Weights & Biases) and deploying models via containers (Docker/Kubernetes) or as serverless functions.
Statistical Logic: You don't just run models; you understand the "why" behind the math and can defend your methodology to technical and non-technical audiences.
Mathematical Depth: Deep expertise in supervised/unsupervised learning, Bayesian statistics, time-series analysis, and causal inference.
Generative AI & LLMs: Working knowledge of integrating LLMs (via LangChain, OpenAI API, or Hugging Face) into business workflows for unstructured data analysis.
The Modern Data Stack: Proficiency in using Snowflake as a feature store and dbt for feature engineering.
Finance Acumen: You understand the levers of a P&L and how predictive modeling impacts revenue and margin.
Communication: Exceptional ability to simplify complex "black box" concepts for executive stakeholders.

Responsibilities

Advanced Technical Execution (The Engine)
Elite Predictive Modeling: Lead the research, design, and deployment of sophisticated models for time-series revenue forecasting, unit economics, and profitability.
Risk & Uncertainty Quantification: Develop advanced statistical tools to predict financial risk and operational volatility, providing the business with probabilistic guardrails.
Algorithmic Innovation: Move beyond standard libraries to build custom ML solutions that address the specific nuances of our finance and project data.
Experimental Design: Architect rigorous A/B and multivariate testing frameworks to measure the causal impact of business decisions and product iterations.
Technical Architecture & Productization (The Bridge)
Model Orchestration: Partner with the Data Engineering team to design the "last mile" of ML deployment—ensuring your models run reliably within our Snowflake/dbt environment.
Seamless Integration: Ensure model outputs are elegantly integrated into Power BI semantic models, turning complex statistical distributions into actionable business signals.
Code Excellence: Set the bar for the Data Science "Playbook," establishing rigorous standards for reproducible research, version control, and model validation.
Strategic & Technical Leadership (The Growth)
Discovery & R&D: Proactively identify opportunities for ML/AI to drive ROI, staying ahead of industry trends in LLMs, deep learning, and predictive analytics.
Abstract Problem Solving: Take high-level business queries from the Director or C-suite and translate them into mathematically sound, executable project plans.
The Intelligence Stack: Expert-level mastery of Python and SQL. Extensive experience deploying production-grade models within a cloud environment (Snowflake preferred).