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.

If you are currently on an EAD or STEM OPT Visa that will eventually require sponsorship for the H1B Visa, unfortunately, we are not able to sponsor.

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, ambiguity, and building new capabilities from the ground up. The successful candidate will be comfortable operating in an evolving environment where data engineering, data science, and solution development responsibilities may overlap.

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.

Unlike traditional data science roles operating within mature data platforms, this position requires a builder mindset. The successful candidate will help shape the underlying data environment while simultaneously developing analytical solutions and models that create business value.

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.

Success in this role requires a willingness to operate across data science, data engineering, and solution development activities while helping build the foundation of Sequoia’s evolving AI and Data capabilities. This is not a narrowly defined data science role within a mature analytics organization.

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
Partner with data engineering resources to help define, validate, and operationalize data pipelines, data models, and analytics-ready datasets
Contribute to the development and evolution of Sequoia’s cloud data and analytics environment
Operate effectively in a rapidly evolving AI and data organization where priorities and requirements may change as new opportunities emerge
Identify gaps in data, infrastructure, and processes and proactively recommend solutions
Balance immediate business needs with long-term platform and analytics objectives

Required Skills/Experience

Bachelor’s degree in Statistics, Data Science, Computer Science, Mathematics, Engineering, or a related field required; advanced degree preferred
3+ years of experience in data science, machine learning, analytics, data engineering, or related technical 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
Demonstrated ability to operate effectively with limited structure and evolving requirements
Familiarity with cloud data platforms, data pipelines, and analytics infrastructure
Experience working across both data science and data engineering disciplines 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
Experience working in startup, consulting, high-growth, or rapidly evolving environments
Experience helping build data platforms, analytics environments, or AI capabilities from early-stage maturity
Experience partnering with business and technical stakeholders to define requirements in ambiguous environments

Competencies

Builder mindset with a willingness to create processes, frameworks, and solutions where none currently exist
Comfortable operating with ambiguity and helping define the path forward
Strong problem-solving skills and ability to balance pragmatism with technical rigor
Highly innovative and willing to challenge conventional approaches
Comfortable learning from failed experiments and pivoting quickly
Ability to influence without authority and work effectively across technical and business teams
Ability to work independently while maintaining strong communication and alignment with stakeholders

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