Data Scientist

Sequoia Financial Group is a growing Registered Investment Advisor (RIA), headquartered in Northeast Ohio, offering financial planning and wealth management services. At Sequoia, we exist with a singular purpose: to enrich lives. Our values define how we behave and guide us through the pursuit of our purpose to enrich lives. At Sequoia, our core values are:

• Integrity. We act in the best interests of others by providing an honest, consistent experience for our clients and team.
• Passion. We pursue our full potential, seeking to continually enhance and evolve our ability to serve our clients and team.
• Teamwork. We subordinate our egos to work together for the benefit of our clients.
  
  

Our promise to team members is that you will grow with us. From experienced advisors to new college grads to transitioning principals, every team member will find Sequoia a place to refine their professional mission, move into new opportunities, go deeper, and lead further. We are built to help you build a career here as a long-term contributor in our work to enrich lives for generations.Summary of the position:

As part of our expanding Data & AI Office, we seek a highly motivated and hands-on Data Scientist to build intelligent models that drive personalization, operational efficiency, and strategic decision-making. This role is ideal for someone who thrives in experimentation, iterative development, and translating business needs into scalable data products.

The Data Scientist will develop product-ready models that support Sequoia’s strategic initiatives in client experience, financial planning, operations, and marketing. This individual will work closely with business stakeholders to understand requirements, translate them into data science problems, and deliver actionable insights through robust modeling and experimentation.

This hands-on role requires technical depth in Python programming, data science workflows, and a strong understanding of mapping business requirements to data models. The ideal candidate will be highly innovative, comfortable with ambiguity, and eager to learn through experimentation and iteration.

This role reports directly to the Vice President of Data and Integrations and collaborates closely with the Data Architect, Client Experience, Marketing, and Technology teams.

Responsibilities

• Develop and deploy predictive and descriptive models using Python and modern data science libraries
• Translate business requirements into data science problems and design appropriate modeling strategies
• Build product-ready models that can be integrated into client-facing and internal applications
• Conduct exploratory data analysis, feature engineering, and model validation
• Collaborate with stakeholders across departments to understand use cases and deliver insights
• Embrace iterative development, rapid prototyping, and continuous learning from experimentation
• Utilize coding accelerators and low-code tools where appropriate to speed up development
• Document modeling decisions, assumptions, and performance metrics for transparency and reproducibility
• Work with data engineers and architects to ensure models are scalable and maintainable in production
• Stay current with emerging techniques in machine learning, generative AI, and financial modeling
  
  

Required Skills/Experience

• Master’s Degree in Statistics
• 1–2 years of experience in data science or machine learning roles
• Proficiency in Python and relevant libraries (e.g., pandas, scikit-learn, NumPy, matplotlib, seaborn)
• Strong understanding of statistical modeling, machine learning, and data preprocessing
• Demonstrated ability to map business requirements to data science solutions
• Experience with iterative development and rapid experimentation
• Familiarity with coding accelerators or low-code platforms (e.g., Azure ML Studio, H2O.ai )
• Excellent communication skills and ability to present findings to non-technical stakeholders
• Strong documentation and organizational skills
• Experience in financial services, banking, or insurance sectors preferred
  
  

Preferred Skills/Experience

• Exposure to cloud-based data science environments (e.g., Azure ML, Databricks)
• Familiarity with tools such as Jupyter Notebooks, Git, and MLflow
• Experience working with Salesforce, Tamarac, eMoney, Fidelity, Schwab, and Box is a plus
  
  

Competencies

• Highly innovative and willing to challenge conventional approaches
• Comfortable learning from failed experiments and pivoting quickly
• Ability to work independently and collaboratively in hybrid work settings