Principal Data Scientist, AI & Automation

Natixis Investment Managers is among the world’s largest asset management firms. Powered by the expertise of more than 15 specialized investment managers globally, we deliver proactive solutions that help clients pursue better outcomes in all markets.
The Distribution Enablement Group (DEG) is an expanding team within Natixis. Our group encompasses Distribution Execution Consulting & Strategy, Distribution Platform Enablement, Data Science & AI, and Data & AI Governance. Our core mission is to drive business growth and improve efficiency by extracting insights from data and streamlining the use of data & technology. We partner closely with Sales, Marketing and other Business teams to simplify & automate workflows, develop actionable analytic reports, generate leads, and more.
As a Principal Data Scientist within DEG, you will be a technical and strategic leader who addresses business goals through data, analytics, automation, and AI. You will collaborate closely with Business partners and IT to design & deliver solutions that support distribution strategy and execution. Partnering with international colleagues, you will help shape technical architecture, standards, and best practices. As both a hands‑on contributor and mentor, you will support skill development across the team and help the group continue to grow, mature, and expand its impact.
This position is based in our Boston office and follows a hybrid work model. Team members currently work in the office a minimum of three days per week, with an anticipated transition to four in-office days per week beginning in 2027. We value the flexibility of hybrid work while recognizing the benefits of in-person collaboration, relationship building, and team engagement.
What you will do:
Help shape DEG's strategy and roadmap across data, analytics, automation and AI, evaluating emerging tools and standards and recommending what to adopt, build or retire.
Partner with Sales, Marketing, Finance and other Business teams to understand their requirements, and translate their goals into scalable data solutions & automated workflows.
Build trust & safety into every solution, including responsible AI use, data quality, evaluation, auditability, and compliance requirements.
Partner with IT to deliver on Business objectives, including deployments & change management, data collection & integration, front-end user experience, and back-end infrastructure.
Design, build and deploy AI-powered and agentic workflows, including multi-agent systems, that automate and augment Business processes, with appropriate orchestration, guardrails and human oversight.
Develop, deploy, and maintain data and analytics pipelines that support business objectives, analytic processes, and interactive dashboards.
Monitor, troubleshoot and optimize analytic and data processes to ensure reliability, performance and availability.
Contribute to technical architecture, documentation and governance throughout the Business related to data, automation and AI.
What you will bring:
Required
7+ years of experience in data science, analytics engineering, or a related role, preferably within financial services or another highly regulated industry.
Track record of setting technical direction or leading initiatives that shaped how a team or business uses data, analytics, automation or AI.
Strong proficiency in Python and SQL, with solid software engineering fundamentals including Git, testing and CI/CD.
Demonstrated experience applying AI to automation pipelines, with attention to reliability, guardrails, evaluation and responsible use.
Demonstrated experience implementing machine learning solutions, including model development, deployment, and ongoing monitoring in production environments.
Experience leveraging cloud platforms or remote compute environments to support data processing and analytics workloads.
Excellent written and verbal communication skills, with the ability to translate complex technical concepts for non-technical stakeholders.
Proven ability to manage multiple priorities, meet deadlines, and remain adaptable in fast-changing environments.
Strong time management skills, attention to detail, and a resourceful, problem-solving mindset.
Preferred
Experience mentoring or coaching other team members.
Experience using dbt for data modeling, data tests and semantic layers.
Experience with workflow orchestration tools (e.g., Airflow).
Familiarity with the Microsoft ecosystem, including services such as Microsoft Foundry and Azure AI Search for AI and agent development, Power BI for reporting, and Power Automate for workflow automation.
Familiarity with the AWS ecosystem, including services such as Redshift, S3, EC2, Lambda, and CloudWatch.
Experience with enterprise data science platforms such as Dataiku (or similar tools).