Sr. Data Scientist

Berkley•Irving, TX

3d•Hybrid

About The Position

As a Senior Data Scientist, you will play a pivotal role in driving data-driven decision-making processes and developing advanced analytical models. This may involve leading or participating in cross functional/operating units’ collaborations to define and develop applications of Artificial Intelligence and Machine Learning towards solving insurance problems. You'll build, refine, and improve existing models as well as develop new techniques and applications to different areas of insurance. The role requires a blend of strong technical expertise, statistical analysis, software engineering and a deep understanding of the insurance industry. What you can expect: Culture of innovation, teamwork, supportive colleagues and leaders willing to invest in talent. Internal mobility opportunities. Visibility to senior leaders and partnership with cross functional teams. Opportunity to impact change. Benefits – competitive compensation, paid time off, comprehensive wellness benefits and programs, employer funded health savings account, profit sharing, 401k, paid parental leave, employee stock purchase plan, tuition assistance and professional continuing education.

Requirements

Master’s degree in either Statistics, Mathematics, Data Science or equivalent quantitative discipline.
5+ years related experience in a Data Scientist role with demonstrated competencies in developing predictive/prescriptive models.
Experience in developing pricing models within the Property and Casualty insurance sector, with a strong grasp of applying actuarial principles to pricing strategies.
Proficiency in programming languages such as Python, R, SQL with the ability to manipulate and analyze structured and unstructured data - including at least one of JavaScript, C#, or other web development tools.
Experience building web applications (R Shiny/ Dash) as a workflow integration product.
Experience building platforms (Flask Applications etc.) to integrate machine learning models as API end points to different technology stacks.
Experience with at least one big data technologies platform (Apache Spark, Snowflake, cloud-based platforms – Microsoft Azure, AWS etc.)
Understanding of NLP & Large Language models with insights on potential business applications in insurance.
Attention to details, a passion for data curiosity and ability to thrive in a dynamic and collaborative environment.
Demonstrated problem solving skills and the ability to approach business challenges with a data driven mindset.
Experience in leading a statistical/actuarial based analytical project.
Demonstrated excellence in problem solving, analytical, research and quantitative analysis skills.
Strong communication skills, written and verbal, including the ability to facilitate meetings and present at all levels of the organization.
Ability to manage multiple projects and timelines across a variety of stakeholders to meet target deliverable deadlines.
Regularly and consistently demonstrates commitment to company values and guiding principles.
Proficient in and have knowledge of Microsoft Office Suite as well as demonstrated ability to learn other software as needed.
Ability to travel on an occasional basis.

Nice To Haves

Actuarial exams certifications in Property and Casualty.
PhD in statistics or quantitative field.

Responsibilities

Collaborate with cross functional teams, including Actuaries, Underwriters, Product Managers to identify and define data science initiatives aligned with BRSS business goals and strategy.
Develop predictive models to assess risks and loss probabilities, enabling effective underwriting decisions and pricing strategies.
Utilize Machine Learning algorithms and statistical methods to analyze insurance trends, customer behaviors and market dynamics.
Design and implement experiments to test hypotheses and provide intuitive solutions to what happened, when, where and why, using data to enhance customer retention and profitable growth.
Create visualizations, dashboards, and slide decks to effectively communicate complex findings to non-technical stakeholders.
Conduct exploratory data analysis to identify patterns, anomalies, and opportunities for process improvements.
Collaborate with data engineers to develop data pipelines and integrate data sources ensuring data quality and integrity.
Build platforms and applications to accelerate data science operations including Model Monitoring and improve process efficiencies of business workflows.
Stay abreast of industry trends, emerging technologies and best practices in data science and insurance analytics.