Data Scientist & Data Engineer

Convergent; United Kingdom; On-site; GBP 74,000 - 223,000; Full time

Job summary

A leading technology firm in the United Kingdom is seeking a Data Scientist & Data Engineer to ensure data quality and build production-grade pipelines. This full-time role requires at least 2 years of experience in data engineering, strong proficiency in Python and SQL, and a collaborative mindset. You will design and maintain data frameworks that enhance user outcomes and product performance. Compensation ranges from $100,000 to $300,000 with an attractive equity package.

Qualifications

• 2+ years of experience in data engineering, data science, or analytics engineering.
• Strong proficiency in Python and SQL for data modeling.
• Hands-on experience with ETL/ELT pipeline construction.

Responsibilities

• Partner with teams to convert user data into insights.
• Design production-grade data pipelines for data ingestion.
• Own the analytics layer including event schemas and metrics.

Skills

Python

SQL

Data modeling

Analytical queries

ETL/ELT pipelines

Data systems

Experimentation

Collaboration

Tools

Airflow

dbt

Spark

Kafka

Job description

This range is provided by Convergent. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$100,000.00/yr - $250,000.00/yr

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.

Responsibilities

• 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 → 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)

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.

Seniority level

Associate

Employment type

Full‑time

Job function

Analyst

Industries

IT Services and IT Consulting