Data Scientist
About 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.
Requirements
- 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
Nice To Haves
- Experience in financial services, banking, or insurance sectors
- 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
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
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
