Data Scientist

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
• Identify gaps in data, infrastructure, and processes and proactively recommend solutions
• Balance immediate business needs with long-term platform and analytics objectives

Requirements

• 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
• 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