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Form of workDir & Data Scientist - GD06BE
We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.
We are looking for candidates with expertise in Natural Language Processing (NLP) to join the Text Factory, a team dedicated to solving text-based problems at the enterprise level. The team is comprised of a cross functional group of linguistic experts, data engineers, data scientists, and product managers.
The ideal NLP Data Scientist candidate for this role will have strong industry experience in working with supervised and unsupervised problems leveraging bag-of-words and deep learning type approaches. They will be skilled in product delivery from ideation, creation, and maintenance and they can guide others in that process. They can perform this work with Python in a cloud environment like Amazon Web Services (AWS) and work with others while doing so. They know how to measure impact model performance connected to financial metrics and can help others understand the importance of doing so.
Sustaining The Hartford's unique workplace culture is vital to delivering on our purpose - underwriting human achievement - and continuously producing outstanding results. Our enterprise work model, which reflects a mix of in-office, hybrid and fully remote roles, helps us attract, retain and develop the talent we need to achieve the company's strategic goals. This role will have a Hybrid work arrangement.
Responsibilities:
- Hands-on development and leading cross-functional projects that include the creation of NLP assets to solve business problems and identify long-term opportunities with measurable outcomes
- Partner with business stakeholders, asset owners, data scientists, and data engineers to support the vision and culture that treats science as a corporate asset
- Advance the department's capabilities by creating and deploying long-term tools to continually evolve the practice of natural language processing
- Improve and follow standards for version control, documentation, software engineering, and statistical science standards for formal peer-review and production grade assets
- Identify and communicate timelines for projects and highlight risks while suggesting mitigations to them
Qualifications:
- Expertise with Python from a functional programming paradigm, able to manage dependencies and virtual environments, along with version control in git
- Expertise in supervised learning and unsupervised learning along with experience in deep learning
- Expertise developing models from inception to deployment
- Experience monitoring models for statistical performance and business impact
- Expertise with Bag-of-Words (BoW) type algorithms and auto-regressive/sequential type algorithms for statistical language modeling
- Experience with embedding algorithms and pre-trained embedding models
- Experience with topic modeling, text mining, sentiment analysis, information extraction
- Experience with cloud computing platforms such as AWS
- Experience helping other scientists do the above
- Expertise helping business users understand the importance of the above
Preferred Qualifications:
- Experience in transfer learning
- Experience with sequential algorithms (e.g., LSTM, RNN, transformer, etc.)
- Graduate degree with an emphasis in research
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:
$130,000 - $195,000
Equal Opportunity Employer/Females/Minorities/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
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