Senior Applied Scientist (Data Science & ML)

Company DescriptionAdZeta turns insights from your data lake to profit signals that seamlessly integrate with your ad stack. We unify your first-party data, predict lifetime value, activate high-value signals, and prove revenue impact— governed, auditable and real-time. AdZeta was built on the premise that data without activation means nothing. Role Description:We’re not hiring a model‑tuner. We’re hiring a builder‑owner.The ideal candidate loves taking ownership, accountability and thrives in 0 → 1 execution.You'll own the entire end‑to‑end ML pipeline and orchestration layer — from raw data → features → models → deployment → monitoring → activation. You’ll ship scrappy v1s fast, then harden to reliable v2s. Zero‑to‑one, then one‑to‑many.Have prior experience working in adtech/martech (highly preferred).What you’ll doData plumbing & schemas: Ingest and model data across Shopify/GA4/ESP/CRM/ad platforms; define event taxonomy, identity graph, and data contracts.Feature engineering: Build offline/online features for pLTV, propensity, and clustering; ensure parity with a feature store (e.g., Feast) and model registry (e.g., MLflow).ML pipeline & training: Stand up reproducible training pipelines (Python/SQL/dbt) with experiment tracking, hyperparameter search, and evaluation.MLOps & deployment: Package models (Docker), deploy to managed services (Vertex/SageMaker/Cloud Run), wire CI/CD (GitHub Actions) and blue‑green/rollback patterns.Orchestration: Schedule jobs with Airflow/Prefect; manage dependencies, SLAs, retries; design backfills and incremental loads.Observability: Implement DQ checks (Great Expectations/dbt tests), model performance & drift monitoring, alerting, and cost guardrails.Activation & APIs: Ship fast, robust services (FastAPI/Cloud Functions) to push signals into Meta CAPI, Google Ads (EC/OCI), TikTok Events API or via reverse ETL (Hightouch/Census).Experimentation & measurement: Partner with product/GTM on uplift tests, holdouts, and calibration to prove incremental value.Docs & enablement: Produce crisp runbooks, ERDs, and playbooks; teach others to self‑serve.30 • 60 • 90 day outcomes30 days: Audit current data + tracking; publish V1 tracking plan & data model; baseline pLTV/propensity notebook and EDA; quick wins on data reliability.60 days: Production warehouse models (dbt) + orchestrated pipelines; first model in staging with offline/online validation; reverse ETL wired for a design partner.90 days: v1 serving in prod with monitoring; value‑based bidding pilot live in Google/Meta for 2+ partners; dashboarding for KPIs and model health; draft case study.You’ll be great at this if youHave 7-10+ years shipping DS/ML systems to production (not just notebooks).Are fluent in Python, SQL, and one major cloud (GCP/AWS), with BigQuery/Snowflake and dbt experience.Know MLOps (MLflow/Weights & Biases, Feast/feature store, Docker/K8s, CI/CD) and orchestration(Airflow/Prefect).Can design measurement plans and communicate trade‑offs with clarity.Thrive in ambiguity, move fast, and own outcomes end‑to‑end.Nice to Have:Streaming (Pub/Sub/Kafka), privacy/consent (Consent Mode v2, GDPR/CCPA), clean rooms (ADH/Meta), Shopify/subscription analytics.Why AdZeta?Founding‑team impact, meaningful equity, and the chance to build the ML backbone of a category‑defining product.How to Apply?DM with: LinkedIn/GitHub, 2–3 bullets on your best 0→1 ML build (problem → approach → impact), and a link to a repo/case study (if public).

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