Datasite is hiring a Director, Data Science (Finance) as the founding architect of our data science function. You will lead the evolution of our intelligence layer, transforming complex business problems into predictive models and experimentation frameworks that bridge abstract problem solving and concrete business outcomes.
What You'll Do
- Directly oversee and contribute to the development of predictive models for revenue forecasting, profitability, and demand planning.
- Architect and deploy tools for predictive financial risk assessment to identify and mitigate volatility.
- Define the vision for how AI/ML will be integrated into the modern data stack (Snowflake/dbt/Power BI) to automate complex decision-making.
- Establish the framework for A/B testing and statistical experimentation to validate business strategies and product changes.
- Serve as the primary partner to the C-suite, translating vague business challenges into structured data science projects with clear ROI.
- Work cross-functionally (Finance, Marketing, Ops) to integrate predictive insights into operational workflows.
- Partner with Data Engineering to ensure models are 'production-ready,' moving them from local scripts to automated, reliable outputs in Power BI.
- Act as a 'Player-Coach' to the current Data Science team while identifying specific skill gaps for future hires.
- Proactively build a network and recruitment strategy for future Data Analytics and Data Science roles.
- Establish the 'Data Science Playbook,' defining standards for code quality, model validation, and documentation.
What We're Looking For
- 10+ years of experience in Data Science or Advanced Analytics.
- 3+ years in a leadership capacity.
- Proven track record of building and deploying predictive models (Revenue, Risk, or Forecasting) that achieved high business adoption.
- Deep expertise in the Python Data Science stack (e.g., scikit-learn, XGBoost, LightGBm).
- Deep experience in time-series forecasting, supervised learning, and causal inference.
- Mastery of libraries dedicated to financial and demand forecasting, such as Prophet, statsmodels, or sktime.
- Experience with model lifecycle management tools (e.g., MLflow, Weights & Biases).
- Experience deploying models via containers (Docker/Kubernetes) or as serverless functions.
- Proficiency in using Snowflake as a feature store and dbt for feature engineering.
- Understanding of the levers of a P&L and how predictive modeling impacts revenue and margin.
- Exceptional ability to simplify complex concepts for executive stakeholders.
- Proven track record of building and deploying predictive models using Python and SQL.
- Hands-on experience with ML orchestration tools and automated testing for model performance.
- Experience with cloud-based ML platforms (e.g., Azure ML, AWS SageMaker, or Databricks) and how they integrate with data warehouses like Snowflake.
- Master’s or PhD in a quantitative discipline (Statistics, Mathematics, CS, Economics, etc.).
Nice to Have
- Working knowledge of integrating LLMs (via LangChain, OpenAI API, or Hugging Face) into business workflows for unstructured data analysis.
- Experience with deep learning frameworks (e.g., PyTorch or TensorFlow).
- Experience hiring, mentoring, or designing a data science function.
- An MBA combined with a technical background.
Technical Stack
- Languages & Libraries: Python, scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Prophet, statsmodels, sktime
- MLOps & Tools: MLflow, Weights & Biases, Docker, Kubernetes, LangChain, OpenAI API, Hugging Face
- Data Platform: Snowflake, dbt, Power BI, SQL
- Cloud Platforms: Azure ML, AWS SageMaker, Databricks
Team & Environment
You will be the founding architect of the data science function, initially leading 1 or 2 Data Scientists, and reporting directly to the C-suite.
Benefits & Compensation
- Salary Range: $167,000.00 - $300,400.00
- Health insurance (medical, dental, vision)
- Retirement savings plan
- Paid time off
- Other employee benefits
- Eligibility for bonuses and commissions
We are an equal opportunity employer and make all employment decisions without regard to race, color, religion, sex, gender identity, sexual orientation, age, national origin, disability, protected veteran status, or any other protected characteristic.
