Data Scientist

Platform: Python/Streamlit production intelligence platform for the Amex Travel Mobile App — centralizes telemetry (Snowflake, ELF/OpenSearch, App Store Connect, Jira, etc.) into dashboards for TLS leadership. Strict 4-layer architecture: Ingestion → Analysis → S3 Storage → Visualization.

Technical Skills

Strong Python 3.11+, pandas, NumPy for data transformation and time-series work

Streamlit dashboard development (multi-page, session state, query-param nav)

API/SQL data integration (Snowflake, OpenSearch, REST sources)

Visualization: plotly, matplotlib, seaborn

AWS (boto3, S3/Parquet), Docker, Kubernetes, CI/CD

Secrets/auth (JWT, cryptography, proxy handling); testing with pytest

Data Science / Analytics

Applied statistics for anomaly detection (rolling median + MAD z-score, thresholds); scipy a plus

Time-series thinking: daily/weekly/monthly frequency-aware baselining

Turns raw telemetry into decision-ready insights (not deep ML, but solid quantitative reasoning)

Business Knowledge

Understands mobile app KPIs across Bookings, Adoption, Engagement, App Health, Card Member Voice, Quality Engineering

Familiar with iOS/Android store metrics, crash/stability, QE/regression concepts

Translates leadership questions into metrics and views

Seniority

Senior-level ownership within an established layered architecture; respects design principles (no business logic in UI, filter-agnostic analysis)

Contributes to and follows Architecture Decision Records (ADRs)

Owns features end-to-end: data source → analysis → dashboard

Communication \& Collaboration

Communicates data-driven findings clearly to leadership and non-technical stakeholders

Works cross-functionally with QE, delivery, and product teams

Strong documentation habits

Go-Getter

Self-directed in integrating new data sources and extending hubs/domains

Curious and metric-obsessed; proactively closes visibility gaps

Bias to ship while keeping the architecture clean.