Senior Data Scientist - Field Business Functions

We are seeking an experienced Senior Data Scientist who excels at connecting business needs with data-driven solutions. The ideal candidate has a proven record of working directly with business leaders, leading stakeholder discussions, structuring ambiguous problems, and translating analytical findings into clear recommendations and measurable outcomes. This position requires strong data science and analytics expertise, but it is specifically designed for someone whose strengths extend beyond technical delivery to include business acumen, stakeholder influence, executive communication, and solution adoption.

Candidates should be comfortable challenging assumptions, guiding decisions, and collaborating with data engineering and technology teams without being primarily focused on backend development.

• Build trusted relationships with business leaders, functional teams, and subject matter experts to understand priorities, operating context, constraints, and decision needs.

• Lead discovery sessions to frame ambiguous business challenges, identify root causes, define hypotheses, and determine where analytics or data science can create meaningful value.

• Translate business needs into clear problem statements, success measures, business requirements, user stories, and analytical plans.

• Challenge requests when the proposed solution does not address the underlying business problem and recommend a more effective approach.

• Align stakeholders on scope, priorities, assumptions, dependencies, risks, and expected outcomes before development begins.

• Own end-to-end analytics and data science initiatives from discovery and data assessment through modeling, validation, implementation, adoption, and outcome measurement.

• Analyze large and complex datasets to identify patterns, drivers, anomalies, opportunities, and actionable insights.

• Design, develop, and evaluate statistical, predictive, and machine learning solutions using methods appropriate to the business question.

• Define evaluation criteria that include analytical performance, interpretability, operational feasibility, business impact, and risk.

• Perform data profiling, cleansing, reconciliation, and validation to ensure analytical outputs are accurate, explainable, and fit for purpose.

• Develop decision-support tools, analyses, dashboards, and prototypes using SQL, Python, R, Power BI, Excel, or related tools.

• Synthesize complex analysis into clear business narratives, options, recommendations, and implications for executive and non-technical audiences.

• Create effective visualizations and presentations that focus on decisions, business impact, tradeoffs, and recommended actions rather than technical detail alone.

• Facilitate stakeholder reviews, resolve conflicting perspectives, and influence decisions using evidence and sound business judgment.

• Communicate limitations, assumptions, uncertainty, and risks transparently so stakeholders can make informed decisions.

• Partner with users to validate outputs, incorporate feedback, drive adoption, and confirm that delivered solutions address the intended business need.

• Manage analytical backlogs, priorities, user stories, acceptance criteria, and delivery progress in Azure Dev Ops within an Agile environment.

• Coordinate with data engineering, application, product, and platform teams on data availability, solution design, implementation, and production readiness.

• Provide analytical leadership without assuming primary ownership for backend platform engineering, pipelines, or infrastructure.

• Mentor Data Scientists and Analysts in business problem…