Data Scientist
Atlanta, Georgia, United StatesAll locationsAtlanta, Georgia, United StatesNew York City, New York, United StatesUnited StatesRaleigh-Durham, North Carolina, United StatesPhiladelphia, Pennsylvania, United States Hybrid
$150,000–$300,000 a yearJobFig found this opening at its original source and checks that it remains available.
About the role
We are an industry-leading startup developing AI for consumer brands. Our solutions leverage machine learning, generative AI, agent-based systems, and graph technologies to get our customers to insights in seconds and to business impact in minutes using our products. We are looking for a Data Scientist to develop the models and analysis behind our products and to prove out their impact with customers, reporting to our Co-Founder & CAIO. As a Data Scientist, you will build the models that turn customer data into decisions — forecasting, optimization, measurement, and the analysis that tells us whether any of it is working. This role is a strong fit for someone earlier in their career who wants real ownership quickly. You'll get direct mentorship from our Co-Founder & CAIO and senior engineers, and the scope to grow fast.
What you'll bring
- Experience developing and validating models on real, messy data — through internships, prior roles, or substantive project work.
- Solid foundation in statistics and machine learning: regression, time series, tree-based methods, experimental design.
- Strong Python (pandas, scikit-learn, and the surrounding ecosystem) and strong SQL.
- Curiosity about the business problem behind the data, not just the modeling technique.
- Clear communication — you can explain what you did, why, and what it means, to someone who doesn't do this for a living.
- Strong problem-solving skills, adaptability, and a "hacker" mentality.
- Eagerness to learn quickly in a startup environment.
- Exposure to CPG, retail, or consumer brand data.
- Experience with Spark, cloud platforms (AWS or similar), or orchestration tools.
- Familiarity with LLMs and their practical use in analytical workflows.