Principal, Data Scientist; Finance

Principal, Data Scientist (Finance) at Datasite

As a Principal Data Scientist, you will serve as the technical lead and primary "engine" of our data science initiatives. Reporting to the Director of Data Science, you will be 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.

Though this is an IC role, you will set technical standards for the department, mentor junior analysts, and architect our predictive ecosystem.

• Advanced technical execution: lead 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 probabilistic guardrails.
• Algorithmic innovation: build custom ML solutions beyond standard libraries to address specific nuances of our finance and project data.
• Experimental design: architect rigorous A/B and multivariate testing frameworks to measure causal impact of business decisions.
• Technical architecture & productization: partner with Data Engineering to design the "last mile" of ML deployment, ensuring reliable model operation within Snowflake/dbt.
• Seamless integration: embed model outputs into Power BI semantic models, transforming statistical distributions into actionable business signals.
• Code excellence: establish standards for reproducible research, version control, and model validation.
• Strategic & technical leadership: identify opportunities for ML/AI to drive ROI, stay ahead of industry trends in LLMs, deep learning, and predictive analytics.
• Abstract problem solving: translate high‑level business queries from the Director or C‑suite into mathematically sound project plans.

• Deep expertise in Python data science stack (scikit‑learn, XGBoost, Light
  
  GBM) and deep learning frameworks (PyTorch or Tensor Flow).
• Predictive expertise in time‑series forecasting, supervised learning, and causal inference. Mastery of libraries such as Prophet, stats models, or sktime.
• MLOps & deployment experience with model lifecycle tools (MLflow, Weights & Biases) and deployment via Docker/Kubernetes or serverless functions.
• Statistical logic: strong understanding of the math behind models and ability to defend methodology.
• Mathematical depth: supervised/unsupervised learning, Bayesian statistics, time‑series analysis, causal inference.
• Generative AI & LLMs: working knowledge of integrating LLMs (Lang Chain, OpenAI API, Hugging Face) into business workflows.
• Modern data stack: proficiency with Snowflake as a feature store and dbt for engineering.

• Finance acumen: understanding of P&L levers and how predictive modeling impacts revenue and margin.
• Communication: ability to simplify complex "black‑box" concepts for executive stakeholders.

• 8–10+ years of experience in Data Science, delivering high‑impact predictive models in a corporate or production environment.
• Proven IC leadership: experience as a staff or principal contributor who has led large‑scale technical projects from concept to deployment.
• Deep familiarity with Snowflake, dbt, and Power BI ecosystem.
• Track record of building and deploying predictive models using Python and SQL with high business adoption.
• Experience with ML orchestration tools and automated testing for model performance (monitoring for data drift and model decay).
• Cloud infrastructure experience (Azure ML, AWS Sage Maker, Databricks) and integration with data warehouses.
• Advanced education:
  PhD or Master’s in physics, mathematics, statistics, computer science, economics, or related quantitative field.

The base salary range represents the estimated low and high end for this position based on a good faith assessment of the role and market data at the time of posting. Compensation may vary and is determined by geographic region, skills,…