Senior Applied Scientist (Data Science & ML)

Company Description

AdZeta 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 do

• Data 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 outcomes

• 30 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 you

• Have 

  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?

meaningful equity

How to Apply?

0→1