Job Overview
- Automating bespoke analyses into repeatable, parameterized workflows.
- Writing and maintaining SQL queries in Snowflake to surface key audience and inventory insights.
- Performing rigorous QA on data products and model outputs to ensure accuracy.
- Providing first-line analytical support for internal teams and clients.
- Developing measurement methodologies for transit and rail inventory.
Core Responsibilities
- Write and maintain SQL queries against our Snowflake warehouse to surface audience, market, format, and inventory insights (percentages, indices, lift), following established methodology and conventions.
- Contribute to the development and validation of measurement methodologies for transit, rail, and other OOH inventory.
- Build lightweight statistical and ML models, and apply sound statistical approaches (indexing, normalization, period comparisons, basic causal/lift logic) to real OOH data.
- Automate recurring analyses — turn one-off SQL and notebook work into parameterized, repeatable pipelines or agentic tool calls and workflows
- Test and QA data products, pipelines, and model outputs; identify discrepancies and validate results before they reach clients.
- Provide first-line support for internal and client-facing data and analytics questions.
- Produce clean, well-documented, reproducible code (documented methodology, concise context for coding agents) that others can pick up and extend.
- Communicate findings clearly — contextualize and frame insights rather than dumping raw data.
Required Technical Experience
- BS in Computer Science, Statistics, Data Science, Mathematics, or a related quantitative field.
- Solid foundation in statistics and machine learning — you understand the methods, not just the libraries, and have trained and evaluated lightweight models.
- Proficiency in Python and its core data libraries (e.g., pandas, NumPy, scikit-learn).
- Moderate to strong SQL skills and comfort working with large, relational datasets.
- Proficiency in coding agents/IDE’s
- Attention to detail; you check your work and question results that look off.
- Clear written and verbal communication; able to explain analyses to technical and non-technical audiences.
- Self-directed and curious, comfortable in a fast-moving, small-team environment.
What Success Looks Like
- You reliably produce clean, well-documented analyses in SQL and Python that the team and clients trust.
- Recurring manual analyses become automated, reproducible workflows that no longer require a senior person to run.
- Our data products and models ship with fewer defects because you catch issues through disciplined testing and QA.
- Data and analytics questions get answered quickly and accurately increasing without the input of senior data engineers
- Technical Development — taking on more modeling, more ownership, and more independent analysis over the engagement
Nice to Have
- Experience with Snowflake (or another cloud data warehouse).
- Experience with LLM APIs and integrations.
- Experience building MCPs and external facing LLM applications
- Geospatial data analysis experience (H3, GIS, spatial joins, lat/lon work).
- Exposure to the out-of-home, ad-tech, or marketing-measurement domain.
- Familiarity with attribution, lift, or causal-inference concepts.
- Dashboard or data-visualization experience.
We are an equal opportunity employer and value diversity at our company. We do not
discriminate on the basis of race, religion, color, national origin, gender, sexual orientation,
age, marital status, veteran status, or disability status.
