Data Scientist

We are:

Goodie AI is the pioneering LLM visibility and AI search optimization platform enabling the world’s top brands to own their AI narrative across leading LLMs like ChatGPT, Gemini and Perplexity. Backed by strong funding and validated by active paying customers, we are scaling fast and tackling some of the hardest AI search challenges.

After you apply, check out Goodie AI to learn even more!

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.

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.

Requirements

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.

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 NoGood team members, recruiters, and HR employees. Our team will contact you regarding job opportunities from email addresses ending in @nogood.io or @higoodie.com. Additionally, we do utilize our ATS- Workable- 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!