Principal Data Scientist

Natixis Investment Managers•Boston, MA

52d•Hybrid

About The Position

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 the Distribution Enablement Group, you will be a technical and strategic leader who addresses business goals through data, automation, and AI. You will collaborate closely with Business partners and IT to design & deliver solutions that support distribution strategy and execution. From building and maintaining analytic- and data- pipelines to powering initiatives such as client lead scoring for Sales, your work will help simplify and evolve workflows across Business teams. 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 is a hybrid position located in our Boston, MA office. We require employees to be in the office a minimum of 3 days a week. While we do not have fixed days in the office, we ask that team members make a best effort to be in the office for important business meetings.

Requirements

7+ years of experience in data science, analytics engineering, or a related role, preferably within financial services or another highly regulated industry.
Strong proficiency in Python and SQL, with experience using Git for version control and collaboration.
Demonstrated experience implementing machine learning solutions, including model development, deployment, and ongoing monitoring in production environments.
Solid understanding of machine learning techniques, statistical analysis, and data modeling concepts.
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.

Nice To Haves

Experience with enterprise data science platforms such as Dataiku (or similar tools).
Familiarity with the AWS ecosystem, including services such as Redshift, S3, EC2, Lambda, and CloudWatch.
Experience with workflow orchestration tools (e.g., Airflow).
Experience using dbt for analytics engineering and data transformations.
Familiarity with workflow automation tools such as Microsoft Power Automate.
Experience working with CRM platforms, such as Salesforce.
Experience integrating and consuming data via REST APIs.

Responsibilities

Partner with Sales, Marketing, Finance and other Business teams to understand their requirements, and translate their goals into scalable data solutions & automated workflows.
Partner with IT to deliver on Business objectives, including production code deployments, data collection & integration, front-end user experience, and back-end infrastructure.
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.
Support the incorporation and responsible use of AI across Business workflows and analytic use cases.
Continually investigate new tools and techniques, as well as emerging standards.