Senior Data Scientist

Description

Envestnet is transforming the way financial advice is delivered through its connected technology, advanced insights, and asset management solutions – backed by industry-leading service and support. Since 1999, Envestnet has served the wealth management industry and today supports trillions in platform assets, serving over a hundred thousand financial advisors. The vast majority of the nation’s leading banks, the largest wealth management and brokerage firms, and over 500 of the largest RIAs rely on Envestnet’s wealth management platform and solutions to drive business growth, boost productivity, and deliver better financial outcomes for their clients.

Envestnet’s Strategy:

• Deliver the industry-leading wealth management platform, powered by advanced data and insights
• Leverage our scale and efficiencies to serve our clients’ needs comprehensively
• Enable financial advisors to deliver more holistic advice – reflecting a more complete view of their clients’ financial lives, and in a more connected environment

For more information, please visit www.envestnet.com.

Job Summary:

We are seeking a Senior AI/Data Scientist to design and deploy machine learning solutions that power Envestnet’s products and tools. This role combines advanced analytics, AI/ML techniques, and wealth domain expertise to deliver intelligent, scalable solutions. The position also requires cross-functional collaboration with engineering and quality assurance teams to ensure successful implementation and delivery.

Job Responsibilities:

• Work independently or in team to solve complex problems and create scalable models/algorithms that will be integrated into Envestnet’s tools and products
• Develop and deploy advanced ML models (predictive analytics, NLP, LLM-based solutions) to enhance Envestnet’s products and tools.
• Engineer features and transform data from complex wealth datasets (accounts, tax-lots, corporate actions) to ensure model accuracy and compliance.
• Leverage knowledge graph techniques (RDF/OWL, SPARQL, Cypher) for entity resolution, semantic modeling, and graph-based insights (e.g., householding, cross-sell).
• Implement retrieval-augmented generation (RAG) and LLM-driven solutions for intelligent content and advisor-facing insights.
• Ensure model governance and compliance by applying explainability (SHAP), fairness checks, and regulatory considerations (Reg BI, GDPR/CCPA).
• Collaborate with engineering and QA teams to integrate models into production using scalable pipelines (Airflow, dbt, MLflow).
• Stay current with AI/ML advancements and apply best practices in MLOps, model monitoring, and drift detection.
• Influence product strategy through actionable insights and clear communication with senior leadership and stakeholders.
• Contribute to the development of the data science product strategy by incorporating input from business leaders, data leaders, and senior leadership, while supporting the execution and alignment of the product roadmap.
• Adherence to and application of Envestnet legal, compliance, risk, business continuity and administrative policy within the role and department(s) including the timely completion of training & awareness, affirmations and testing as requested.
• As part of the responsibilities for this role, you will understand and readily support Envestnet's established corporate business practices, policies, internal controls and procedures designed to create value or minimize risk.

Required Qualifications:

• 5+ years in Data Science, Machine Learning, or AI roles.
• Proficiency in Python, SQL, and ML frameworks (scikit-learn, XGBoost, NLP/transformers).
• Experience with large datasets, statistical modeling, and cloud ML platforms (AWS, Snowflake).
• Familiarity with wealth management domain and regulatory context (Reg BI, GDPR/CCPA).
• Knowledge of graph technologies (Neo4j, RDF/OWL, SPARQL) and MLOps tools (MLflow, Airflow, dbt)1) Wealth domain fluency
• Deep understanding of client → household → advisor hierarchies; legal entities; account types (brokerage, advisory, qualified plans), tax-lots, corporate actions, fee schedules, and billing.
• Product knowledge: equities/ETFs/mutual funds/SMAs/UMAs/alternatives; model portfolios; rebalancing & overlay.
• Regulatory context: Reg BI, SEC/FINRA recordkeeping, KYC/AML basics, privacy (Reg S-P, GDPR/CCPA).
• Core workflows: onboarding/householding, performance (GIPS concepts), goal-based planning, suitability/risk profiling, surveillance, and advisor productivity.

Preferred Qualifications:

• Experience with LLM integration and retrieval-augmented generation (RAG) for enterprise applications.
• Expertise in graph algorithms (community detection, link prediction) and embeddings (node2vec, graphSAGE).
• Strong understanding of model risk management, including explainability (SHAP), fairness, and compliance implications.
• Hands-on experience with orchestration tools (Airflow, Prefect) and CI/CD for ML pipelines.
• Familiarity with industry standards (FIBO, FIX, ISO 20022) and semantic modeling for wealth data.
• Ability to translate complex AI outputs into business insights and communicate effectively with senior stakeholders.

Envestnet:

• Be a member of an innovative and industry leading financial technology and solutions company
• Competitive Compensation/Total Reward Packages that include:
• Health Benefits (Health/Dental/Vision)
• Paid Time Off (PTO) & Volunteer Time Off (VTO)
• 401K – Company Match
• Annual Bonus Incentives
• Parental Stipend
• Tuition Reimbursement
• Student Debt Program
• Charitable Match
• Wellness Program

Envestnet is an Equal Opportunity Employer.

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