Senior Applied Scientist

3 weeks ago

Hubli, India AdZeta Full time

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? Founding‑team impact, meaningful equity , and the chance to build the ML backbone of a category‑defining product. How to Apply? DM with: /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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