Data Scientist

Berkley•Houston, TX

About The Position

The Data Scientist designs, builds, and delivers analytical solutions that support underwriting, pricing, and operational decision-making. The role performs exploratory data analysis, feature engineering, data pipeline development, and predictive modeling, working closely with business and technical partners to ensure solutions are accurate, scalable, and aligned with business goals.

Requirements

2–5 years of experience in data science or a related analytical field, including exposure to model deployment and monitoring.
Strong sense of ownership, urgency, and self-motivation;
Excellent written and verbal communication skills; able to convey complex concepts clearly.
Effective collaborator with experience in cross-functional, team-oriented environments.
Prior quantitative research experience through academic work, personal projects, or previous roles.
Proficiency in Python (pandas, NumPy, scikit‑learn) and SQL; solid understanding of databases and data modeling.
Experience conducting exploratory data analysis, including profiling, handling missing data, and outlier detection.
Feature engineering experience, including geospatial, temporal, and derived features.
Familiarity with version control (e.g., GitHub) and cloud analytics platforms (e.g., Databricks).
Understanding of Agile or SDLC practices.
Master’s degree in data science, analytics, statistics, computer science, engineering, or related field.

Nice To Haves

Familiarity with Oil & Gas or Property & Casualty insurance concepts is a plus

Responsibilities

Partner with business stakeholders to define analytical needs and prototype solutions
Evaluate the business value of internal and third-party data sources using standardized assessment criteria.
Build foundational understanding of relevant insurance and energy domain concepts.
Conduct Exploratory Data Analysis to assess data quality, structure, coverage, and predictive potential.
Build and refine data pipelines using SQL and Python.
Develop entity-matching methods, including geospatial and temporal techniques.
Engineer and maintain features that support analytical and predictive modeling.
Build and evaluate predictive models, comparing performance against benchmarks.
Quantify expected business value, costs, and ROI for proposed solutions.
Design repeatable workflows for modeling, experimentation, and evaluation.
Collaborate with engineering teams to integrate analytical models into production systems.
Implement monitoring to ensure data and model quality over time.
Identify opportunities for iteration and performance improvement based on results and business feedback.
Work with cross-functional teams to clarify requirements and acceptance criteria.
Prepare analytical datasets, dashboards, and reports that support decision-making.
Communicate insights clearly to technical and non‑technical stakeholders.
Conduct quality assurance checks on datasets, metrics, and models.
Maintain documentation for data sources, features, models, and workflows.
Automate repetitive or manual tasks using scripting and AI tooling.