Lead Data Scientist

Crisis24•Annapolis, MD

About The Position

Crisis24 is a global, AI-enhanced provider of travel risk management, mass communications, critical event management, crisis-security consulting, personal protection solutions and global medical concierge capabilities, allowing prominent organizations, disruptive brands and influential people to operate with confidence in an uncertain world. At Crisis24, we go beyond mere employment; we pave the way to a realm where your skills become instrumental in shaping global security, guiding clients through a multifaceted and challenging landscape. Your journey with us will be deeply fulfilling, driven by a powerful sense of purpose and accomplishment. Within our thriving environment, you'll discover abundant chances for both personal and career advancement. Seize this moment to push your limits, broaden your expertise, and elevate your professional journey to unprecedented levels. Join the Crisis24 team today and be a part of something extraordinary where growth and impact converge. More information is available at www.crisis24.garda.com Who We Are Looking For As a Lead Data Scientist in our AI pipeline team, you will build pipeline that identifies and alerts customers to emerging risks. This role focuses on designing and deploying advanced data pipelines that combine unstructured data with structured data. You will maintain and enhance models that detect and highlight potential risks, enabling proactive decision-making for our customers. As the Lead Data Scientist, you will set the vision for our data science initiatives, lead a team of data scientists, and collaborate closely with engineering and product teams to build scalable, high-impact solutions. You will ensure the quality, accuracy, and interpretability of our risk detection models while driving innovation in natural language processing, geospatial analysis, and event detection in a fast-paced, mission-critical environment.

Requirements

Master’s or Ph.D. in Computer Science, Statistics, Mathematics, Engineering, Data Science, or a related quantitative field.
8+ years of experience in data science, machine learning, or advanced analytics, with a proven track record of successful project delivery and leadership.
Expertise in statistical modeling, ML, deep learning, data/text mining, and Gen AI prompting.
Proficient in Python and SQL; experienced with large-scale, real-time data.
Skilled in AWS and MLOps tools for model deployment and monitoring.
Proven experience building and deploying production-grade ML systems.
Strong knowledge of data visualization tools and methods.
Excellent critical thinking and creative problem-solving skills.
Committed to ethical data science and data privacy regulations.

Nice To Haves

Expertise in advanced NLP and text analytics, including entity recognition, event detection, and multilingual modeling.
Experience with information retrieval and web data mining, leveraging large-scale scraping, search relevance, and vector databases.
Familiarity with knowledge graphs and relationship modeling to enhance risk intelligence and entity linkage.
Strong understanding of MLOps and scalable ML systems using tools like Airflow, Docker/Kubernetes, and cloud ML platforms.
Proven leadership and communication skills, with the ability to mentor teams and translate analytical outputs into business insights.

Responsibilities

Manage LLM production in AWS Bedrock and OpenAI, including deployment, scaling, monitoring, and cost optimization.
Perform hands-on coding and development work.
Mentor data scientists, review code, and enforce best practices.
Translate business goals into model KPIs and measurable impact.
Prioritize resources and plan data science roadmaps.
Define strategy for analytics and data science initiatives.
Collaborate with leadership and product teams on data-driven opportunities.
Lead end-to-end data science projects from design to deployment.
Present insights to both technical and non-technical audiences.
Drive innovation and process improvement through data science.
Design and validate analytical models using diverse tools.
Ensure compliance with data privacy, security, and ethical standards.
Integrate LLM solutions into enterprise workflows with reliability and security.