Data Science Engineer
Company : Nautilus Insurance Company
Location : Chesterfield, MO, 63017
Job Type : Full Time / Part Time
Date Posted : 14 January 2026
Company Details
Why MEC?
At Midwest Employers Casualty (MEC), we combine the stability of a Fortune 500 company with the agility of an innovative team. We are passionate about improving the quality of life for employees severely injured on the job and helping companies understand and mitigate risk. Our culture values collaboration, curiosity, and continuous learning. If you want to make an impact, work on meaningful projects, and grow your career in a supportive environment, MEC is the place for you.
Company URL: https://www.mecasualty.com
Responsibilities
As a Data Science Engineer, you will focus on leveraging the company’s substantial data assets to deliver actionable and meaningful business insights using analytics, predictive models, machine learning, and artificial intelligence. The ideal candidate will possess a strong technological background with entry-to-mid-level expertise in data analysis, data engineering, building (and deploying) predictive models. We are looking for a highly motivated individual with the ability to help us deliver next-generation analytical products to our internal and external business partners.
This position requires supporting our application infrastructure, writing code, analyzing data, creating models, creating ad hoc apps/reports/dashboards, building automated workflow, working with both internal and external customers, and participating in project management and execution.
The Advanced Analytics team members are passionate about continuous learning and turning that knowledge into cutting-edge analytical solutions. We strive to deliver innovative analytical tools that generate tangible business results.
Key functions include but are not limited to:
- Deliver high-quality code, predictive models, data analysis, and data visualizations using best practices
- Assist with analytical solution development, testing, and deployment
- Assist with model development, testing, and deployment
- Service the data needs of customers by providing analysis, reports, dashboards, and/or electronic files
- Analyze internal and external data to explore business problems and document findings
- Act as a liaison between the Advanced Analytics Team and other departments
- Actively assist with project management and project execution
Qualifications
- Bachelor’s or Advanced degree in data science, computer science, mathematics, statistics, engineering, physics or other sciences (or college degree with significant coding experience in a corporate setting).
- Education should include significant work in advanced mathematics, statistics, computer science, and/or data science.
- Requires 0 to 3 years of business experience as a data/software/machine learning engineer, data scientist, or in a related field.
- Beginner-to-intermediate level understanding of SQL Databases (SQL Server, Oracle, Databricks, etc.) and the SQL Programming Language (T-SQL, PL/SQL, SparkSQL, etc.).
- Experience with working with large databases for analytical purposes. This would include both transactional systems and data warehouse systems.
- Beginner-to-intermediate level programming experience with Python.
- Knowledge of coding best practices.
- Knowledge of object-oriented programming and design patterns.
- Experience building AI solutions using Large Language Models such as ChatGPT, including RAG systems, Agents, and/or automated workflows.
- Experience with a variety of statistical and machine learning algorithms including classification and regression modeling, deep learning, and ensemble methods.
- Knowledge of data visualization best practices.
- Can take high-level project requirements and formulate timelines, milestones, and tasks as part of project execution and provide accurate project status reports to management.
- Knowledge of information technology practices relating to data quality, data warehousing, ETL processes and data mining.
- Understanding of project management practices and approaches including Agile methodologies.
- Experience with project management software such as Jira, Trello, etc.
- Experience with code management (version control) techniques utilizing tools such as BitBucket, GitHub, etc.
- Experience with cloud computing ecosystems such as Microsoft Azure a plus.
- Experience with Databricks a plus.
- Experience with CI/CD frameworks such as Jenkins or GitHub workflows a plus.
- Experience with UNIX and Docker/Kubernetes a plus.
- Excellent listening and interpersonal skills.
- Ability to multitask and manage assigned work under tight deadlines.
- Possesses an intense curiosity and determination to explore and solve complex business problems utilizing data, technology, and analytical techniques.
- Experience working in a team-oriented, collaborative environment.
- Ability to translate very complex subject matter into clear written and oral communications.
Additional Company Details
We do not accept any unsolicited resumes from external recruiting agencies or firms. 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.Related Jobs
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Frequently asked questions
For Data Science Engineers in Chesterfield, proficiency in Python is essential due to its extensive libraries for machine learning and data analysis. Additionally, knowledge of SQL variants like T-SQL or SparkSQL is valuable for handling large datasets common in insurance analytics.
Chesterfield shows growing interest in data-driven roles, especially in insurance sectors. While larger cities like St. Louis have more openings, Chesterfield offers competitive opportunities with a focus on mid-sized companies seeking analytics talent, making it an attractive market for emerging Data Science Engineers.
Daily tasks often include developing predictive models, automating data workflows, collaborating with business teams to interpret analytics, and managing data infrastructure. Engineers also support internal tools, ensuring data quality and delivering actionable insights tailored to insurance risk management.
Absolutely, Nautilus values collaboration and continuous learning, fostering an environment where Data Science Engineers can innovate. This culture encourages skill development in advanced analytics and AI, accelerating career advancement within a stable yet agile company framework.
Certifications like Microsoft Azure Data Engineer and Certified Analytics Professional are well-regarded locally, especially given Nautilus’s cloud and analytics focus. These credentials can enhance credibility and signal readiness to handle complex data engineering challenges in regional insurance firms.
Data Science Engineers in Chesterfield typically earn between $75,000 and $95,000 annually, depending on experience and skills. Given Nautilus Insurance Company’s market position, compensation aligns with regional tech salary benchmarks and includes benefits reflective of a Fortune 500-affiliated employer.
The company actively employs AI, including large language models like ChatGPT, to build predictive models and automated workflows. Data Science Engineers contribute by designing, testing, and deploying these solutions, directly influencing risk assessment and client-facing analytics products.
Nautilus blends the stability of a Fortune 500 with an innovative culture, providing Data Science Engineers unique exposure to insurance-specific data challenges. The role emphasizes cross-department collaboration, project management, and cutting-edge AI integration, setting it apart from typical data roles.
Proficiency in project management platforms is important as Data Science Engineers at Nautilus coordinate analytics projects and track progress. Familiarity with Jira or Trello helps streamline task management, ensuring timely delivery of data solutions aligned with business goals.
Focusing on intermediate SQL skills, Python programming, and foundational machine learning algorithms will be beneficial. Additionally, gaining experience with cloud platforms like Azure and understanding CI/CD workflows can improve your readiness for Nautilus’s technical environment.