Data Scientist

ABOUT GOODIE AI:

Goodie helps leading brands win AI search. As billions of people use ChatGPT, Perplexity, Gemini, and other AI systems to discover products and make buying decisions, brands need a way to understand and influence how they’re represented.

Goodie gives teams a full AI control plane: real-time visibility into how AI models speak about their brand and products, how competitors show up, and an optimization engine to improve visibility and performance. This category didn’t exist two years ago - we were early, and we’re defining it.

We’re backed by strong investors, trusted by category-leading customers, and scaling fast. We’re hiring curious, ambitious builders to help shape the future of AI search.

What we are looking for:

Goodie AI is searching for a talented and ambitious Data Scientist to join our growing team! Goodie helps brands win visibility and revenue across AI search, LLMs, and agentic commerce. You will be the point person turning messy multi-model signals into measurement, forecasts, and optimizations that our product can act on. If you enjoy building models that ship and change customer behavior, you will like this seat.

What you’ll do:

• Work with large datasets. Own efficient querying, cleaning, labeling, and taxonomy alignment for brands, SKUs, and categories.

• Design sampling and classification strategies that turn noisy LLM outputs and crawler logs into reliable brand and product insights.

• Use LLMs and NLP to extract structure from unstructured text at scale. Topics include query fan-out, sentiment, citation extraction, and entity linking for brands, products, and creators.

• Define product-grade metrics. Create durable definitions for visibility score, answer coverage, product presence, and agentic checkout readiness.

• Build and run experimentation frameworks. A/B tests, holdouts, counterfactuals, and uplift modeling to quantify impact on citations, share of voice, and conversions.

• Develop and refine predictive models that analyze and forecast AI search behavior across models and surfaces.

• Translate complex findings into clear decisions. Partner with the founding team to inform roadmap, pricing, and customer playbooks.

• Create evaluation harnesses. Establish automatic evals and human-in-the-loop labeling for model quality, bias, and drift across LLM providers.

• Detect anomalies. Build monitors for crawler behavior, rankings, and feed health to catch regressions before customers do.

What you have:

• 3 to 7 years in applied analytics or data science within tech, marketing, or ads. Startup or high-growth experience preferred.

• Strong Python and SQL. Comfortable in notebooks and in code reviews.

• Skilled with sampling and inference. Stratified sampling, bootstrapping, extrapolation, reweighting, and variance estimation.

• Solid ML toolkit. Time series, classification, regression, weak supervision, and methods to estimate event frequency from partial observations.

• Practical LLM knowledge. Strengths in prompt design, structured extraction, embeddings, and an understanding of model limits and failure modes.

• Curious and current on multi-modal and LLM research. You enjoy reading papers and pressure testing ideas in real data.

• Builder mindset in a fast team. You value clarity, speed, and ownership.

Bonus/Nice to have:

• Experience with large-scale information extraction or search quality

• Background in causal inference, MMM, or attribution models

• Hands-on work with product feeds and retail catalogs

• Contributions to open source or published work we can read

• Deployed side projects we can click through

Our data and modeling canvas:

• Problems: AI search measurement, AEO scoring, agentic commerce readiness, product catalog and feed integrity, ranking and citation shifts, attribution for AI traffic

• Signals: LLM responses, crawler and agent logs, SERP and AI answer snapshots, product feeds, marketplace metadata, GA4 and GSC connectors, CRM data

• Targets: Share of voice, citation count, answer coverage, SKU presence, conversion lift, time-to-value for optimizations

Tech stack you will touch:

• Languages: Python, SQL

• Libraries: pandas, NumPy, scikit-learn, PyTorch or TensorFlow, Hugging Face, spaCy.

• Data: Postgres or AlloyDB, BigQuery, dbt, DuckDB for local work

• Production ML/MLOps: model serving (FastAPI/Flyte/Batch jobs), CI/CD, versioning, experiment tracking (MLflow/Weights & Biases), monitoring & alerting for performance/drift.

• Cloud & data tooling: AWS/GCP/Azure, containers (Docker).

• Models and providers: OpenAI, Anthropic, Google, Meta, Mistral, Perplexity, together with internal eval harnesses

BEWARE OF FRAUD! Please be aware of potentially fraudulent job postings or suspicious activity by persons that are posing as Goodie team members, recruiters, and HR employees. Our team will contact you regarding job opportunities from email addresses ending in @nogood.io http://nogood.io or @higoodie.com http://higoodie.com. Additionally, we do utilize our ATS- Ashby- to help us schedule initial screening calls. Job seeking is hard- we’re sorry that scammers have added this element to your search for something new. Stay vigilant out there!