Hartford Funds is seeking a Data Scientist to join their Emerging Tech and AI team to create value through intelligent systems and automation by developing and deploying statistical and machine learning models, contributing to AI and automation initiatives, and supporting scalable data solutions.
Requirements
- Strong foundation in statistical modeling and machine learning, including regression, time series forecasting, tree-based algorithms, optimization, and causal analysis.
- Strong Python and SQL.
- Understanding of the Microsoft Copilot ecosystem; including Copilot Studio, Azure Foundry, and prompting best practices.
- Experience working with Snowflake or a similar cloud data platform.
- Ability to work with APIs, write modular and reproducible code, and support basic data engineering tasks (e.g., pipeline design, version control).
- Familiarity with robotic process automation tools such as UiPath, Power Automate, or similar platforms.
- Awareness of workflow automation tools (e.g., n8n, Apache Airflow, etc) and how they integrate with predictive models or LLMs.
Responsibilities
- Develop and deploy statistical and machine learning models to solve a range of business problems, including forecasting, classification, segmentation, and causal inference.
- Contribute to AI and automation-related initiatives, helping evaluate and implement GenAI and workflow solutions.
- Support scalable data solutions using modern cloud tools and APIs, and help maintain analytical and data science codebases.
- Collaborate across business and technology teams to scope projects, identify opportunities, and deliver actionable insights.
- Ability to work with APIs, write modular and reproducible code, and support basic data engineering tasks (e.g., pipeline design, version control).
Other
- Minimum 4 years of hands-on experience in data science or a related field.
- Preferred 6+ years of hands-on experience in data science or a related field.
- Strong communication skills with the ability to translate technical work into clear, business-relevant insights.
- Ability to balance hands-on technical development with broader strategic thinking.
- Curiosity about emerging technologies and a pragmatic approach to applying them in real business contexts.
