Data Scientist
Lensa
Job Overview
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Job Description
Data Scientist
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. We invite you to grow your career with us, experience our caring culture, and enjoy work-life balance. Here, there's no limit to what you can achieve.
Sedgwick has been recognized by Newsweek as America's Greatest Workplaces National Top Companies, certified as a Great Place to Work®, and listed among Fortune's Best Workplaces in Financial Services & Insurance.
Sedgwick is the world's leading risk and claims administration partner, helping clients thrive by navigating the unexpected. The company's expertise, combined with the most advanced AI-enabled technology, sets the standard for solutions in claims administration, loss adjusting, benefits administration, and product recall. With over 33,000 colleagues and 10,000 clients across 80 countries, Sedgwick provides unmatched perspective, caring that counts, and solutions for the rapidly changing and complex risk landscape. For more, see sedgwick.com
Primary Purpose of the Role
To develop and manage predictive modeling assignments through completion; to communicate results; to make recommendations to management; and to ensure model ownership, implementation, and monitoring; to spot trends and to gain maximum insight that can give the company a competitive advantage.
Are You An Ideal Candidate?
We are looking for enthusiastic candidates who thrive in a collaborative environment, who are driven to deliver great work, are customer-oriented and are naturally empathetic.
Essential Responsibilities May Include
- Execute data science projects as identified by Sr Data Scientists, leadership, and stakeholders
- Help to build, prototype, and deploy machine learning models in sandbox and production environments
- Produce reports to communicate metrics and outcomes of stakeholder interest
- Assist with developing and maintaining end-to-end ETL data pipelines
- Work to explore new internal data sources and their utility within projects
- Communicate modeling challenges, findings, and outcomes to stakeholders
Qualifications
- Insurance claims knowledge preferred
- Minimum one year of predictive modeling, data science, and analysis experience with a solid background in using data visualization tools and libraries and data exploration, data wrangling, and feature engineering
- Experience writing Python or R code, and notebook environments
- Knowledge of common unsupervised/supervised ML techniques
Taking Care of You
- Career development and promotional growth opportunities.
- A diverse and comprehensive benefits offering including medical, dental, vision, 401k, PTO and more.
Sedgwick is an Equal Opportunity Employer and a Drug-Free Workplace. If you're excited about this role but your experience doesn't align perfectly with every qualification in the job description, consider applying for it anyway! Sedgwick is building a diverse, equitable, and inclusive workplace and recognizes that each person possesses a unique combination of skills, knowledge, and experience. You may be just the right candidate for this or other roles.
Key skills/competency
- Predictive Modeling
- Machine Learning
- Data Analysis
- Python
- R Programming
- ETL Data Pipelines
- Data Visualization
- Feature Engineering
- SQL
- Statistical Modeling
How to Get Hired at Lensa
- Research Sedgwick's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor.
- Tailor your resume: Customize your resume to highlight predictive modeling, machine learning, and data pipeline experience relevant to Sedgwick's needs.
- Showcase your data science skills: Prepare to discuss projects involving Python, R, ETL, and data visualization, aligning with the Data Scientist role.
- Understand Sedgwick's business: Gain insights into risk and claims administration to demonstrate how your data science expertise can benefit the company.
- Practice behavioral interviews: Be ready to share examples of collaboration, problem-solving, and delivering data-driven recommendations in a team setting.
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