About the Role
Trellis is hiring a Senior Data Scientist to drive exploratory analysis and model development for data-scarce and emerging problems in our Real-Time Bidding (RTB) and pricing systems. You’ll focus on understanding the limits and potential of smaller datasets – helping the team quickly assess which projects are viable, what data needs to be improved, and where machine learning can scale impact.
You’ll design and conduct exploratory studies, create transparent analytical reports, and build research-grade models that inform business decisions and evolve into production-ready systems. This role sits at the intersection of analytics, modeling, and product – collaborating closely with ML engineers, software engineers, and BI to turn ambiguous problems into scalable solutions.
This is a fully remote position, based in the US or Canada, reporting directly to the Head of RTB, Thomas Boquet.
We believe in paying competitively for great work. As a remote team, we set our salaries at the 75th percentile of national market data in our industry. You can count on your pay being in line with (or above) the market. For this role, the base salary range is $210,000-225,000. We review compensation twice a year to make sure we’re staying competitive and rewarding our team’s contributions.
What You’ll Do
- Perform deep exploratory analysis to assess project feasibility and uncover data quality or coverage issues.
- Build clear, reproducible Jupyter or Colab notebooks that communicate insights visually and narratively.
- Partner with product and engineering to understand data generation processes and identify gaps.
- Develop statistical and ML models (scikit-learn, statsmodels, TensorFlow, PyTorch) for pricing and bidding use cases.
- Prototype predictive models that can be scaled by the ML engineering team into production systems.
- Leverage Vertex AI or equivalent tools for experimentation, training, and deployment workflows.
- Create visualizations using Python (Plotly, Matplotlib, or Seaborn) to communicate key findings effectively.
- Contribute to a culture of analytical rigor, documentation, and rapid experimentation.
What You’ll Need
- 5+ years of experience as a data scientist or applied researcher, ideally in tech, fintech, or advertising.
- Strong statistical foundation with hands-on experience using scikit-learn, statsmodels, TensorFlow, or PyTorch.
- Proven ability to perform exploratory data analysis (EDA) and communicate findings clearly through notebooks and visualization.
- Experience using Vertex AI, SageMaker, or other ML lifecycle platforms for experimentation and model deployment.
- Fluency in SQL and Python data tooling (pandas, NumPy, Plotly, etc.).
- Deep curiosity about how data is created, transformed, and used to drive business outcomes.
Pragmatic mindset — balancing research depth with real-world impact and iteration speed.