Datasite is looking to solve complex, abstract business problems by transforming them into rigorous predictive models and experimentation frameworks to drive business growth and revenue.
Requirements
- 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.
- 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.
Responsibilities
- High-Impact Modeling: Directly oversee and contribute to the development of predictive models for revenue forecasting, profitability, and demand planning.
- Risk Prediction Tools: Architect and deploy tools for predictive financial risk assessment, helping the business identify and mitigate volatility before it occurs.
- ML/AI Roadmap: Define the vision for how AI/ML will be integrated into our modern data stack (Snowflake/dbt/Power BI) to automate complex decision-making.
- Experimentation Rigor: Establish the framework for A/B testing and statistical experimentation to validate business strategies and product changes.
- Abstract Problem Solving: Serve as the primary partner to the C-suite, translating vague business challenges into structured data science projects with clear ROI.
- Data Productization: Partner with Data Engineering to ensure models are 'production-ready,' moving them from local scripts to automated, reliable outputs in Power BI.
- Team Scaling: Act as a 'Player-Coach' to the current Data Science team while identifying the specific skill gaps (e.g., NLP, Deep Learning, MLOps) needed for future hires.
Other
- Entrepreneurial Spirit: You are excited by the prospect of building a department from the ground up, from selecting tools to hiring the team.
- 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.
- 10+ years of experience in Data Science or Advanced Analytics, with 3+ years in a leadership capacity.
- Master’s or PhD in a quantitative discipline (Statistics, Mathematics, CS, Economics, etc.).
