Data Scientist

Company Details

Berkley Oil & Gas, a W. R. Berkley Company, is an insurance underwriting manager offering specialized property and casualty products and risk services to customers in the energy sector. Our customers value theexpertisewe bring and appreciate working with professionals who understand their business. We are committed to delivering innovative products and exceptionalserviceto our customers, agents, and brokers. Berkley Oil & Gasremainsdedicated to staying informed about the evolving dynamics of the industry, supporting efforts to minimize and mitigate risks in the oil patch, and continually improving our products and services to meet customer needs.

W.R. Berkley Corporation, founded in 1967, is one of the nation’s premier commerciallines ofproperty and casualty insurance providers. Each of the operating units in the Berkley groupparticipatesin a niche market requiring specialized knowledge about a territory or product. Our competitive advantage lies in our long-term strategy of decentralized operations, allowing each of our units toidentifyand respond quickly and effectively.

Company URL: https://berkleyoil-gas.com/

The company is an equal opportunity employer.

Responsibilities

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 areaccurate, scalable, and aligned with business goals.

• Business Understanding & Solution Design

• 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.

• Buildfoundationalunderstanding of relevant insurance and energy domain concepts.

• Data Discovery, Exploration & Engineering

• Conduct Exploratory Data Analysisto assess data quality, structure, coverage, and predictive potential.

• Build andrefinedata pipelines using SQL and Python.

• Develop entity-matching methods, including geospatial and temporal techniques.

• Engineer andmaintainfeatures that support analytical and predictive modeling.

• Model Development & Experimentation

• Build and evaluate predictive models, comparing performance against benchmarks.

• Quantify expected business value, costs, and ROI for proposedsolutions.

• Design repeatable workflows for modeling, experimentation, and evaluation.

• Deployment, Integration & Monitoring

• Collaborate with engineering teams to integrate analytical models into production systems.

• Implement monitoring to ensure data and model quality over time.

• Identifyopportunities for iteration and performance improvement based on results and business feedback.

• Collaboration, Communication & Project Delivery

• 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 andnon‑technicalstakeholders.

• Quality, Documentation & Automation

• 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.

Qualifications

• Experience & Professional Skills

• 2–5 years of experience in data science or a related analytical field, including exposure to model deployment and monitoring.

• Strong senseof ownership, urgency, and self-motivation;

• Excellent written and verbal communication skills;ableto convey complex concepts clearly.

• Effective collaborator with experience in cross-functional, team-oriented environments.

• Priorquantitative research experience through academic work, personal projects, orpreviousroles.

• Technical Skills

• Proficiencyin Python (pandas, NumPy,scikit‑learn) andSQL;solid understanding of databases and data modeling.

• Experience conducting exploratory data analysis, including profiling, handling missing data, andoutlier 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 ofAgile or SDLC practices.

• Domain Knowledge

• Familiarity with Oil & Gas or Property & Casualty insurance concepts isa plus

Education Requirement

Master’s degreein data science,analytics,statistics,computer science, engineering, or related field.

Additional Company Details

We do not accept any unsolicited resumes from external recruiting agencies or firms. The company offers a competitive compensation plan and robust benefits package for full time regular employees. The actual salary for this position will be determined by a number of factors, including the scope, complexity and location of the role; the skills, education, training, credentials and experience of the candidate; and other conditions of employment.

Sponsorship Details

Sponsorship not Offered for this RoleThe 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 areaccurate, scalable, and aligned with business goals. - Business Understanding & Solution Design - 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. - Buildfoundationalunderstanding of relevant insurance and energy domain concepts. - Data Discovery, Exploration & Engineering - Conduct Exploratory Data Analysisto assess data quality, structure, coverage, and predictive potential. - Build andrefinedata pipelines using SQL and Python. - Develop entity-matching methods, including geospatial and temporal techniques. - Engineer andmaintainfeatures that support analytical and predictive modeling. - Model Development & Experimentation - Build and evaluate predictive models, comparing performance against benchmarks. - Quantify expected business value, costs, and ROI for proposedsolutions. - Design repeatable workflows for modeling, experimentation, and evaluation. - Deployment, Integration & Monitoring - Collaborate with engineering teams to integrate analytical models into production systems. - Implement monitoring to ensure data and model quality over time. - Identifyopportunities for iteration and performance improvement based on results and business feedback. - Collaboration, Communication & Project Delivery - 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 andnon‑technicalstakeholders. - Quality, Documentation & Automation - 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.Mid-Senior Level