**Candidates who live within 50 miles of Boston, MA; Portsmouth, NH; Seattle, WA; Columbus, OH; or Plano, TX will follow a hybrid schedule, coming into the office two days per week. Otherwise, this role is remote with occasional travel. **
As an Assistant Director you will:
• Apply knowledge of sophisticated analytics techniques to manipulate large structured and unstructured data sets to generate insights to inform business decisions.
• Lead end-to-end development of new predictive models for high-impact business outcomes (e.g., improving claims handling efficiency): frame and test hypotheses, design statistically rigorous experiments, assemble/label training data, engineer features, and train/validate models.
• Build state-of-the-art ML systems that leverage structured data, unstructured text, and generative AI.
• Select and implement appropriate algorithms and evaluation methods to deliver measurable accuracy and business value.
• Follow ML Ops best practices to create organized code repos, production-quality code, and reproducible results.
• Stay up-to-date with the latest advancements in data science and machine learning, and apply them to solving complex problems in the insurance claims domain.
• Provide technical mentorship and guidance to junior data scientists.
• Responsible for larger components of projects of moderate to high complexity.
• Communicate findings through technical presentations, reports, and recommendations to both technical and non-technical stakeholders.
• Participate in cross-functional working groups and contribute to the broader data science community to promote best practices.
Preferred skills and experience:
• Broad conceptual understanding and practical knowledge of the end-to-end data science lifecycle.
• Exceptional hands-on data science technical skills (e.g. SQL, Python, and Statistical Inference).
• Experience collaborating with non-technical stakeholders to understand which problems need solving, design solutions, and bring them to market.
• Experience working with complex Type II data to assemble training datasets to appropriately model operational processes.
• Proficiency in Python and MLOps practices, with experience in version control (Git), code review, collaborative development workflows (e.g., GitHub/GitLab), and model versioning/experiment tracking (e.g., MLflow).
Additional skills and experiences that are nice to have:
• Knowledge of claims handling processes and experience working with claims data.
• Experience developing LLM-based solutions for production use cases.
• Practical experience with cloud platforms like AWS (preferably), Google Cloud, or Azure.
• Familiarity with data pipeline and workflow management tools like Airflow, among others.
Qualifications
- Broad knowledge of predictive analytic techniques and statistical diagnostics of models.
- Expert knowledge of predictive toolset; reflects as expert resource for tool development.
- Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
- Networks with key contacts outside own area of expertise. Ability to establish and build relationships within the aligned functional area or SBU.
- Ability to give effective training and presentations to peers, management and less senior business leaders.
- Ability to use results of analysis to persuade team or department management to a particular course of action.
- Has a value driven perspective with regard to understanding of work context and impact.
- Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and a minimum of 2 years of relevant experience, a Master`s degree (scientific field of study) and a minimum of 4 years of relevant experience or may be acquired through a Bachelor`s degree (scientific field of study) and a minimum of 5+ years of relevant experience.
