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