Job 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.

Requirements

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 monitoringiteration. 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).

Benefits

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.