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USE YOUR DATA MODELING EXPERIENCE TO HELP DRIVE INFORMED BUSINESS DECISIONS
Why you should join our Modeling team:
As a Principal Data Scientist on the Modeling team, you will work under the Risk function alongside talented team members in pricing, risk analytics, and decision engines, while closely collaborating with the Cyber Research, Insurance and Tech Product, Claims, and Underwriting teams. The Modeling team at At-Bay has defined our aggregation view, developed an aggregation risk model, and supported reinsurance and finance in driving profitable growth. We are seeking a key team member to unlock new possibilities in attritional modeling, enhancing our segmentation and underwriting strategy. In this exciting role, you will be the first to apply data scientist knowledge and skill set to pioneer new methodologies in the attritional side of the risk domain, playing a crucial part in shaping At-Bay’s risk model.
You’ll join a growing team of data scientists and modelers of diverse backgrounds and report to our Director in Risk Modeling (Yoshi Yamamoto). You’ll be surrounded by a team that loves what they do, leverages technology to improve efficiency & minimize duplicative work, and recognizes the enormous responsibility that they have – to support key business decisions with data backed insights and a deep understanding of insurance risk.
Role overview:
Your work will directly contribute to At-Bay’s risk assessment framework that helps with data-driven decisions involving millions of dollars of exposure. This is a multidisciplinary role that includes developing deep knowledge of the intersection between cyber security and insurance, business acumen, research, and analytical skills. This is also a hands-on technical role not only for leading and managing projects but also conducting data analysis/modeling end to end.
At-Bay is in a unique position owning cyber security data and cyber insurance data including claims. You will be responsible for developing an end to end data analytics platform. With the platform, you will lead the data analytics/research project on all the data available connecting to cyber security and cyber insurance, and provide the insight from data to cross-functional stakeholders including but not limited to Risk, Insurance & Tech Product, Underwriting, and Claims teams.
How you’ll make an impact:
By 3 months…
By 6 months...
What you’ve accomplished already:
Full Time
Building Construction
$197k-240k (estimate)
06/06/2024
08/05/2024
bayltd.com
BILLINGS, MT
1,000 - 3,000
1981
Private
ROBERT C BLAIR
$200M - $500M
Building Construction
Bay officesconstruction, fabrication, and maintenance services.
The job skills required for Principal Data Scientist include Data Science, Machine Learning, Analysis, Python, Insight, SQL, etc. Having related job skills and expertise will give you an advantage when applying to be a Principal Data Scientist. That makes you unique and can impact how much salary you can get paid. Below are job openings related to skills required by Principal Data Scientist. Select any job title you are interested in and start to search job requirements.
The following is the career advancement route for Principal Data Scientist positions, which can be used as a reference in future career path planning. As a Principal Data Scientist, it can be promoted into senior positions as a Data Science Manager that are expected to handle more key tasks, people in this role will get a higher salary paid than an ordinary Principal Data Scientist. You can explore the career advancement for a Principal Data Scientist below and select your interested title to get hiring information.
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Step 2: Knowing the best tips for becoming an Accountant can help you explore the needs of the position and prepare for the job-related knowledge well ahead of time.
Career tips from people on Principal Data Scientist jobs
Experience manipulating large data sets through statistical software.
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Data scientists need to be critical thinkers, to be able to apply objective analysis of facts on a given topic or problem before formulating opinions or rendering judgments.
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Maintain clear and coherent communication, both verbal and written, to understand data needs and report results.
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Understand the business question and clarify related data aspects, such as types of data to collect and time frame.
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