A Data Analyst develops computer programs to analyze large customer information databases for companies and organizations. Analyzes data to identify patterns and provide information relevant to a particular business, industry or field; analysis may be used for marketing, or to detect fraud in financial transactions, or for research. Develops computer programs to protect confidential customer information.
Dynamic Specialty Inc. is seeking a strategic, technically expert leader to architect and advance our data and analytics capabilities. The Lead Architect - Data & Analytics Services will design and oversee the data infrastructure, predictive modeling, and analytics platforms that power underwriting, claims, and risk decisions across our commercial trucking insurance programs. This individual will translate telematics, safety, and loss data into predictive models that sharpen risk selection and pricing, while building the data architecture and governance framework that supports Dynamic Specialty Inc.'s growth as a data-driven MGA. Join us in shaping the analytics backbone of a fast-growing commercial trucking specialty insurer. Essential Duties /
Responsibilities:
Define the enterprise Data & Analytics architecture and technology roadmap. Design modern architectures for trusted, governed, and reusable data and analytical services. Lead the evolution from descriptive reporting toward predictive and prescriptive analytics. Identify and architect opportunities for machine learning, generative AI, and AI-enabled decision systems. Enable analytical and AI capabilities to be consumed through applications, APIs, dashboards, workflows, and intelligent agents. Evaluate emerging data and AI technologies and recommend those that provide meaningful business value. Establish standards for data quality, lineage, security, governance, model evaluation, and responsible AI. Partner with business leaders to translate business problems into data-driven, predictive, and AI-enabled solutions.
Knowledge / Skills
/ Abilities (KSA's): Teamwork — Balances team and individual responsibilities; welcomes feedback and diverse viewpoints; builds a positive team spirit; prioritizes team goals over individual recognition; helps sustain morale and commitment to shared analytics objectives; supports others' success. Interpersonal Skills — Resolves conflict constructively without blame; maintains confidentiality of data, models, and strategic information; listens without interrupting and seeks clarification; regulates emotions under pressure; remains open to new ideas and analytical approaches. Ethics — Treats people with respect; honors commitments; acts with integrity and transparency; upholds organizational values and data governance standards; models ethical conduct in data use, modeling, and reporting. Problem Solving — Identifies and resolves issues in a timely manner (e.g., data quality gaps, model drift, pipeline failures); gathers and analyzes data skillfully; develops and evaluates alternative modeling approaches; collaborates effectively to address discrepancies; uses reason even when topics are high-stakes or technically complex. Technical Skills — Understands data architecture, predictive modeling, and cloud platform best practices; continually develops expertise in analytics tools and machine learning techniques; shares knowledge with others; applies internal review processes to safeguard data integrity. Oral Communication — Speaks clearly and professionally in varied settings; listens and confirms understanding; responds to questions with confidence; presents model performance and analytics insights effectively to technical and non-technical audiences; contributes constructively in meetings. Written Communication — Writes clearly, concisely, and accurately in technical documentation, model specifications, and reports; reviews work for accuracy and clarity; adapts tone to audience; presents performance data effectively; interprets governance and compliance requirements accurately. Quality Management — Seeks opportunities to standardize and improve data and analytics workflows; demonstrates accuracy and thoroughness in model development and deployment; applies feedback to enhance results; monitors outputs to ensure consistency and completeness across systems. Organizational Support — Follows policies and procedures (including data privacy and regulatory requirements); completes administrative and project tasks accurately and on time; aligns work with company goals and values; supports diversity, equity, and inclusion. Strategic Thinking — Understands how data and analytics efforts impact underwriting, claims, and business performance; identifies risks, trends, and opportunities to improve risk selection and profitability; adjusts data strategy as the business and technology landscape evolve. Judgment — Makes timely, well-reasoned decisions (including what to build, prioritize, or escalate); explains rationale clearly; involves the appropriate stakeholders; exercises sound judgment in handling sensitive or high-impact data and models. Professionalism — Engages others with tact and respect; remains composed under pressure (e.g., tight deployment timelines, model performance issues); accepts responsibility for outcomes; follows through on commitments; sets a professional example for the team. Quality — Demonstrates precision and attention to detail in data pipelines, models, and reporting; looks for ways to enhance accuracy and efficiency; incorporates feedback; self-audits work before deployment to ensure high-quality standards. Attendance/Punctuality — Is reliable and punctual; ensures coverage of responsibilities when absent (including time-sensitive model monitoring); arrives prepared for meetings and deadlines. Dependability — Follows instructions and responds to direction; takes ownership of actions; keeps commitments; meets project deadlines or communicates alternative plans promptly. Initiative — Volunteers solutions and improvements; pursues self-development (staying current on data platforms and modeling techniques); seeks increased responsibility; takes appropriate, calculated technical risks; asks for and offers help when needed. Innovation — Applies creative thinking to streamline data workflows and improve predictive accuracy; meets challenges with resourcefulness; proposes practical architecture and modeling improvements; communicates ideas effectively to gain buy-in. Leadership & Staff Development — Provides clear direction and expectations; mentors and develops data engineers and analysts; delegates effectively; monitors progress; fosters accountability and continuous learning. Data/Analytics Acumen — Understands risk, underwriting, and claims dynamics as they relate to commercial trucking; links data and modeling activity to business and financial outcomes; supports planning and forecasting with accurate data and insights. Systems Proficiency — Uses cloud data platforms, ETL/ELT tools, and BI/analytics dashboards effectively; supports data integrity and consistency; collaborates on tool and process improvements and adopts technology to enhance efficiency.
Minimum Qualifications:
Required:
Bachelor's degree in Data Science, Computer Science, Statistics, Actuarial Science, or a related field (Master's degree preferred). 8+ years of progressive experience in data architecture, data engineering, or predictive analytics, including experience in a technical leadership role. Demonstrated experience building and deploying predictive or machine learning models in a production environment. Strong proficiency in SQL, Python or R, and modern cloud data platforms (e.g., AWS, Azure, GCP, Snowflake, Databricks). Experience designing and managing ETL/ELT pipelines and data warehouse or data lake architectures. Insurance industry background, ideally within commercial auto/trucking or other P&C lines.
Preferred:
Experience with telematics, ELD, or
CSA/SAFER
data specific to commercial trucking. Actuarial or underwriting analytics background, or close partnership experience with actuarial teams. Familiarity with BI/visualization tools (Power BI, Tableau) and advanced spreadsheet skills (Excel, including pivot tables and data modeling). Relevant certifications (e.g., AWS/Azure/GCP data or machine learning certifications, cloud architect certifications). Experience within an MGA or program administrator environment. Demonstrated ability to build a data/analytics function from the ground up or scale an existing one. Strong collaboration skills and ability to work effectively with Underwriting, Claims, Actuarial, IT, and executive leadership.
Physical Demands and Work Environment:
Continually required to sit for extended periods while working at a computer analyzing data and reviewing models. The noise level is moderate and typical of a busy office environment. Position is hybrid, with regular on-site collaboration expected at the Coral Springs, FL office. Standard business hours, with occasional extended hours required during model deployments, quarter-end, year-end close, audits, or system rollouts. Limited travel may be required for vendor meetings, data provider evaluations, or collaboration with other company locations.
