Description
By joining Sedgwick, you'll be part of something truly meaningful. It's what our 33,000 colleagues do every day for people around the world who are facing the unexpected.
As a Senior Data Scientist, you will play a critical role in developing and implementing data-driven solutions to support various functions across the company. You will collaborate with cross-functional teams to analyze complex datasets, build predictive models, and provide actionable insights that drive business strategy and operations.
Responsibilities:
- Provide technical mentoring and code reviews for junior team members.
- Design, deploy and monitor machine learning models in production environments.
- Analyze large and complex datasets to uncover trends, patterns and insights, with data storytelling.
- Lead technical solution design for data science initiatives.
- Support data science POC and use case development, with commercial business value estimation.
- Develop and implement predictive models to enhance claim management, customer experience, quality assurance and other aspects of the business.
- Collaborate with leaders and other stakeholders to understand business needs and deliver data-driven solutions.
- Liaise with the leadership team on translating general enquires into experimental designs to test hypotheses and improve decision-making processes.
- Communicate findings and recommendations to non-technical stakeholders through clear and concise reports and presentations.
- Stay current with the latest advancements in data science, machine learning and general tech trends that would be applicable in Insurance.
- Ensure adherence to good practices and maintain thorough documentation throughout the projects lifecycle.
- Assist the team in raising awareness to Data Science and Advanced Analytics throughout the business, showcasing they might benefit from it.
- Support the reporting function of the team by building and maintaining reports in PBI and SSRS, and responding to requests for data.
Person Specification:
- Tertiary qualification in relevant field (e.g. Data Science, Computer Science).
- Proven experience in data analysis, statistical modelling and machine learning.
- Proficiency in programming languages such as Python, R and SQL.
- Experience with machine learning frameworks (scikit-learn, tensorflow, keras, time series etc).
- Experience with visualization tools (ggplot, plotly, matplotlib, seaborn, etc)).
- Strong problem solving skills and the ability to think critically and creatively.
- Excellent communication skills, being able to effectively collaborate with both technical and non-technical stakeholders.
- Knowledge of best practice.
- Attention to detail.