Lead Data Scientist

About the Company

Vantage Group Holdings Ltd. (Vantage) was established in late 2020 as a re/insurance partner designed for the future. Driven by relentless curiosity, our team of trusted experts provides a fresh perspective on our clients’ risks. We add creativity to tech-enabled efficiency and robust analytics to address risks others avoid. Vantage provides specialty re/insurance through its operating subsidiaries in Bermuda and the U.S.

Vantage has approximately 350 colleagues in both the United States and Bermuda. We have offices in Chicago, IL, Norwalk, CT, Arlington, VA, Boston, MA, New York, NY and Hamilton, Bermuda. Additionally, we are a highly geographically diverse workforce with colleagues based in 35 states and counting. We fully support work flexibility including remote and hybrid work arrangements.


About the Role:
At Vantage, we see data as our most important asset after our people. The Lead Data Scientist is a critical role responsible for defining and executing our overall data science and AI strategy, creating practical solutions and leveraging analytics to help our Re/Insurance businesses see risk differently. Reporting to the Chief Data, Analytics and Technology Officer, this role will lead a team of accomplished data scientists and will be a key member of the Senior Leadership Team.
You will have deep Re/Insurance business acumen and a proven track record of delivering solutions that unlock business value. You will have a keen understanding of how to meet leaders where they are when it comes to properly leveraging data science, create partnerships across the organization to design and deliver outstanding results, and know when a simple solution will engage colleagues more than a complicated one. You will not only have a passion for Data Science, but a passion for delivering business value, and collaborating with your colleagues.
The base salary expectation for this role is between $240,000 and $300,000. Actual base salary for the selected candidate may be higher commensurate with candidate experience and expectations. Additionally, Vantage offers its colleagues performance-based bonus potential, strong health & welfare benefits, retirement plans with company match, competitive time off plans, a highly flexible work environment, and much more.

Responsibilities & Accountabilities:

• Define the strategy and roadmap for developing data science and AI solutions across Vantage.
• Be a strategic thought partner; driving the business problem solving process and creating solutions that deliver business value.
• Measure usage of solutions and drive ongoing enhancements to deliver business value.
• Oversee the development of sustainable solutions using machine learning techniques and other analytical methods.
• Present model results to the senior leaders and end users across the organization to drive adoption.
• Perform cost-benefit analyses to determine the value of a project and assist in estimating the return on investment of deployed analytical solutions.
• Remain current on industry trends, tools, and techniques.

Ideal Candidate Profile:
• MBA, Master’s or Ph.D. STEM preferred.
• 15-20 years of leadership experience in a Re/Insurance company, preferably in Data Science or Data Analytics.
• Experience bringing groups together to design, understand and best leverage end to end data science solutions.
• Ability to build strong relationships with leaders from across the organization, developing trust and mutual respect.
• Make decisions around data insights to enable Re/Insurance leaders to deploy them and capitalize on them as quickly as possible.
• Ability to discuss data science in terms that colleagues of all levels will understand to grow overall knowledge and capability within the organization.
• Proven track record of developing practical strategies that unlock business opportunities.
• Strong knowledge of Python and/or R.
• Hands-on experience with version control systems such as git.
• Experience with both supervised (e.g., GLM, RF, GBM, XGBoost) and unsupervised learning methods.
• Ability to work with data engineers and ML ops engineers to operationalize data science assets.

Competencies:
• Business Acumen: understands business implications of decisions; displays orientation to profitability; demonstrates knowledge of market and competition; aligns work with strategic goals.
• Change Management: develops workable implementation plans; communicates changes effectively; builds commitment and overcomes resistance; prepares and supports those affected by change; monitors transition and evaluates results.
• Design: generates creative solutions; translates concepts and information into images; uses feedback to modify designs; applies design principles; demonstrate