Senior Applied Scientist

2 weeks ago

Varanasi, India Whatjobs IN C2 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 adesign 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: 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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