Energy Jobline ZR
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. We are committed to ensuring that we offer the most exciting career opportunities from around the world for our jobseekers.
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 .
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.
This position is not eligible for immigration sponsorship.
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
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
If you are interested in applying for this job please press the Apply Button and follow the application process. Energy Jobline wishes you the very best of luck in your next career move.
#J-18808-Ljbffr
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 .
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.
This position is not eligible for immigration sponsorship.
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
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
If you are interested in applying for this job please press the Apply Button and follow the application process. Energy Jobline wishes you the very best of luck in your next career move.
#J-18808-Ljbffr