Convergent

Compensation

Full Description

This is a foundational, high-impact role at the core of Convergent’s AI platform. As a Data Scientist & Data Engineer, you’ll own the end-to-end data and experimentation backbone that powers our adaptive simulations and human-AI learning experiences. You’ll build reliable pipelines, define data products, and run rigorous analyses that translate real-world interactions into measurable improvements in model performance, user outcomes, and product decisions. You will Partner with product, AI/ML, cognitive science, and frontend teams to turn raw telemetry and user interactions into decision-ready datasets, metrics, and insights. Design and build production-grade data pipelines (batch + streaming) to ingest, transform, validate, and serve data from product events, simulations, and model outputs. Own the analytics layer: event schemas, data models, semantic metrics, dashboards, and self-serve data tooling for the team. Develop and maintain offline/online evaluation datasets for LLM-based experiences (e.g., quality, safety, latency, user outcome metrics). Build experiment measurement frameworks: A/B testing design, guardrails, causal inference where applicable, and clear readouts for stakeholders. Create feature stores / feature pipelines and collaborate with ML engineers to productionize features for personalization, ranking, and adaptive learning. Implement data quality and observability: anomaly detection, lineage, SLAs, automated checks, and incident response playbooks. Support privacy-by-design and compliance: PII handling, retention policies, and secure access controls across the data stack. 2+ years of experience in data engineering, data science, analytics engineering, or a similar role in a fast-paced environment. Strong proficiency in Python and SQL; comfortable with data modeling and complex analytical queries. Hands-on experience building ETL/ELT pipelines and data systems (e.g., Airflow/Dagster/Prefect; dbt; Spark; Kafka/PubSub optional). Experience with modern data warehouses/lakes (e.g., BigQuery, Snowflake, Redshift, Databricks) and cloud infrastructure. Strong understanding of experimentation and measurement: A/B tests, metrics design, and statistical rigor. Familiarity with LLM-adjacent data workflows (RAG telemetry, embeddings, evaluation sets, labeling/synthetic data) is a plus. Comfortable operating end-to-end: from ambiguous problem definition → implementation → monitoring → iteration. Clear communicator with a collaborative mindset across product, design, and engineering. Nice to have Experience with real-time analytics and event-driven architectures. Knowledge of recommendation/personalization systems and feature engineering at scale. Experience with data privacy/security practices (PII classification, access controls, retention). Compensation varies based on profile and experience, but a general cash range (fixed comp + performance variable) is $100,000–$300,000, plus a very competitive equity package.