Remote Computer Science Teaching Jobs in Connecticut

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The Hartford seeks a Data Scientist within a multidisciplinary actuarial/data science team responsible for designing and delivering powerful analytical insights utilizing statistical modeling, machine learning, and data engineering techniques to enhance and overhaul core actuarial processes. Our team is a cross-functional team of data scientists, data engineers, and actuaries working in a highly collaborative environment which involves heavy interaction with partner-customers across actuarial, product, and data science.

We seek candidates with a strong foundation in statistical modeling and/or machine learning who have a drive for finding novel and efficient solutions to maximize business value.As a data scientist, you will participate in the entire model lifecycle partnering with cross-functional business and technical partners to understand business strategies and design, develop, implement, and evolve loss modeling solutions. Our team culture prioritizes flexibility and innovation, so an aptitude and desire to research state-of-the-industry solutions is a must, along with an aptitude to learn new tools and languages as necessary. This role will involve communicating model results to varying business partners so strong communication and presentation skills are required.

This role can have a Hybrid or Remote work arrangement depending on experience and skillset. Candidates who live near one of our office locations will have the expectation of working in an office 3 days a week (Tuesday through Thursday). Candidates who do not live near an office and have appropriate experience may be eligible for a remote work arrangement, with the expectation of coming into an office as business needs arise.

Responsibilities:

• Create statistical models, algorithms, and machine learning techniques to enhance traditional actuarial processes and assumptions

• Think creatively to envision how we can enhance long-standing actuarial methodology using statistical modeling and machine learning techniques

• Use R and/or Python to build, maintain, and support loss models across different lines of business

• Participate in reviewing work with business partners and team members on an ongoing basis to calibrate deliverables against expectations and effectively translate results to business initiatives

• Develop knowledge of The Hartford's formal and informal structures, business processes, and data sources

• Participate in the creation and deployment of long-term tools to continually evolve the business

• Contribute to the successful implementation of strategies to achieve targeted business objectives

• Remain current on research techniques and become familiar with state-of-the-art tools applicable to your function

Qualifications:

• 2+ years of relevant experience recommended

• Preference for at least one of:

  • Master's in Statistics, Applied Mathematics, Data Science, Computer Science, Actuarial Science, or a similar analytical field

  • Progress towards relevant professional credentials (e.g. FCAS, FSA, CSPA)

• Experience in statistical modeling, inference, and building machine learning algorithms in Python and/or R

• Experience in SQL and navigating databases to extract relevant attributes

• Experience in Unix, Git, Shiny and R Markdown is a plus

• Experience in the end-to-end modeling lifecycle, from requirements gathering to monitoring and validation

• Able to communicate effectively with both technical and non-technical audiences

• High level of independence, but is also a team player that demonstrates a high level of ownership

• Results driven with commitment to meeting deadlines

This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT or Charlotte, NC) 3 days a week (Tuesday through Thursday).

Candidates must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.

Compensation

The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford's total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:

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