Senior Data Scientist
- 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.
- Conduct user interviews and journey walkthroughs to surface real problems; apply behavioral insights to translate ambiguous needs into quantified problem statements and value hypotheses.
- Build prioritization frameworks (impact, effort, risk, data readiness, compliance) and size ROI for use cases.
- Partner with Finance/PMO to track realized value vs. forecast post-launch
- Rapid Prototyping & MVP Development
- Design POCs using LLMs, RAG, prompt engineering, and classical ML; evolve into MVPs with clear success criteria and guardrails.
- Conduct feature engineering, algorithm selection, and set monitoring plans for drift and bias
- Package prototypes with evaluation harnesses; enforce clear "continue/pivot/stop" decision gates
- Enterprise Prediction & Model Development
- Build and maintain forecasting and propensity models (churn, next best action, AUM growth, advisor capacity, client lifecycle scoring) serving multiple departments.
- Standardize feature stores across Salesforce and planning/portfolio platforms (Tamarac, Orion, Addepar, Black Diamond)
- Define SLAs/SLOs, feature refresh cadence, and rollback criteria
- Convert model outputs into actionable artifacts, such as decision playbooks, scenario calculators, dashboards, and alerting rules.
- Define workflows, leading indicators, and counter-metrics that drive business actions.
- Create Tableau/Power BI visualizations with clear narratives for non-technical stakeholders.
- Data Science Culture & Governance
- Champion MLOps hygiene: versioning, experiment tracking, model cards, and documentation
- Host office hours and workshops to build data literacy and ethical AI awareness
- Embed responsible-AI principles, RBAC, and human-in-the-loop controls; coordinate with Legal/Compliance on risk and auditability
- Rigorously document Workflows and Processes to establish the AI and Data Office for long-term reliability and resilience
- Maintain a transparent inventory of models and experiments; deprecate low-value assets.
- Establish prioritization scoring frameworks and publish quarterly roadmap updates.
- Track portfolio health: use-case progression, realized value, time-to-decision improvements
- Cross-Functional Partnership
- Coordinate with data engineering on operationalization and with analytics on KPI alignment.
- Collaborate with PMO on experiment time-boxing and capacity management
- Work with vendors on architecture, integration, and build-vs-buy decisions
- 7–12 years in data science, ML, analytics, or product discovery
- Python expertise: pandas, scikit-learn, NumPy; Jupyter notebooks and Git
- Statistical & ML fundamentals: feature engineering, validation, error analysis
- LLM & AI proficiency: POC development with prompt engineering, RAG, and classical ML
- Decision-support framing: translate models into actionable workflows; define KPIs and measure business impact
- Data governance: Data Dictionary, lineage, RBAC, privacy principles
- Communication: strong written/verbal skills with technical and non-technical audiences
- Iterative delivery: comfort with short feedback cycles, A/B testing, and learning from experiments
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