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Data Scientist & Data Engineer at Convergent. 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).. Company Location: United Kingdom.