Analytics and Decision Support Manager

Analytics and Decision Support Manager 

Data Science Reporting and Business Decision Support 

• Reports to: Chief Data and Analytics Officer 
• Department: IDEA — Intelligence, Data, Engineering & Analytics 
• Team structure: Two Team Leads and four Analysts across Data Science and Business Data Support 

Position Summary 

• The Analytics and Decision Support Manager is the senior hands-on leader for enterprise analytics, data science, reporting, and business decision support. Reporting to the CDAO, this role manages through two Team Leads, each responsible for two Analysts. Data Science owns analytical methods, predictive model development, experimentation, and model-performance evidence; Business Data Support owns reporting, self-service analytics, recurring and ad hoc business analysis, and practical support to business users.
• The Manager sets the analytical agenda, ensures that metrics and methods are trustworthy, and turns ambiguous business questions into evidence, recommendations, and reusable decision tools.
• The role must be equally comfortable challenging a model, reviewing a KPI definition, advising an executive, and personally conducting high-priority analysis. 

Core Responsibilities 

Lead Analytics Teams 

• Manage, coach, and develop two Team Leads and four Analysts, with clear roles, quality standards, feedback, and accountability. 

• Prioritize demand based on business impact, urgency, data readiness, and capacity, with clear ownership and stakeholder communication. 

• Own the enterprise analytical agenda and resource plan; hire, assess performance, develop Team Leads, and build succession coverage for critical analytical capabilities. Maintain regular hands-on involvement in priority delivery. 

Deliver Data Science and Decision Support 

• Translate business questions into statistical analyses, predictive models, experiments, dashboards, forecasts, scenario models, and recommendations that support specific decisions. 

• Ensure deliverables are timely, understandable, and actionable and clearly explain assumptions, limitations, and material changes in results. Supply model evaluation and reproducible performance evidence for BLV and other intelligence products; Intelligence Products owns product requirements, acceptance criteria, deployment into use, and outcomes. 

• Advise senior leaders on findings, uncertainty, and trade-offs; challenge business assumptions and analytical methods, and build reusable models and decision tools. 

Govern Metrics and Quality 

• Establish consistent metric definitions, technical evaluation, documentation, and review practices for reports, models, and analytical outputs. Business owners approve business meaning and retain ownership of policy, decisions, financial assumptions, forecasts, certifications, and recurring business execution. 

• Partner with data engineering and business teams to improve data quality, automate recurring work, expand appropriate self-service, and retire redundant reporting. Provide technical evidence to support any required independent validation and business approval; model development does not constitute independent validation. 

• Improve the analytics operating model by reviewing demand, decision usefulness, and recurring quality issues; agree improvements with business owners and track adoption. 

Role Expectations 

• This is a senior manager role with substantial individual contribution. The Manager is expected to personally conduct important analyses, develop or review models, interrogate data, define metrics, and write decision recommendations for senior leaders.
• The Manager must also build a scalable operating model for analytics by hiring, coaching, delegating, setting quality standards, managing demand, and developing Team Leads. Team Leads are expected to contribute substantively to delivery as well as supervise their teams. Analyst is the corporate grade for staff performing data science and business data support work. 

Requirements

• Education:
• Relevant education or professional training in statistics, mathematics, economics, finance, engineering, computer science, analytics, or a related quantitative discipline is valued. Demonstrated analytical depth, leadership, and delivery experience are the primary qualifications; a degree is not mandatory.

• Required Experience

• Twelve or more years of progressive experience in data science, analytics, business intelligence, quantitative decision support, or closely related work, including at least five years of people leadership and meaningful experience leading Team Leads, managers, or senior analytical staff.
• Demonstrated success building, scaling, or materially improving an analytics or data science function and delivering analytical capabilities that are actually used in business decisions or production products. Must be able to coach Team Leads, develop strong analysts and data scientists, manage a mixed portfolio of recurring and ad hoc demand, and make prioritization decisions with senior business leaders. Recent hands-on analytical delivery is required; this is not a management-only role.
• Experience owning an analytics portfolio that combines data science, executive/management reporting, enterprise KPIs, self-service analytics, forecasting, scenario modeling, and rapid ad hoc decision support.
• Experience developing and evaluating predictive models with appropriate train/test design, backtesting, performance metrics, stability or drift analysis, documentation, and clear communication of limitations and intended use.
• Experience defining enterprise metrics and reconciling competing definitions across functions, with strong instincts around denominator logic, cohorts, time periods, data lineage, and reproducibility.
• Experience building analytical tools and decision frameworks that move beyond descriptive reporting to support specific operating, credit, pricing, portfolio, capital, or strategic decisions.
• Experience supporting senior leaders in a fast-moving business where analytical demand must be triaged based on decision value, urgency, data readiness, and capacity. Lending, credit, portfolio, or financial-services analytics experience is helpful but not required.
  

• Technical Skills
• Strong hands-on capability with SQL and Python or equivalent analytical tools, plus business-intelligence and visualization platforms.
• Deep working knowledge of statistical analysis, predictive modeling, model evaluation and backtesting, experimentation, forecasting, scenario analysis, feature development, data visualization, and reproducible analytical workflows.
  
  

• Soft Skills

• Exceptional ability to frame ambiguous business questions, identify the decision that analysis must support, select an appropriate analytical approach, and communicate findings, uncertainty, limitations, and trade-offs clearly.
• Must be able to provide concise, practical recommendations to senior executives while also working effectively with operational users and technical teams.
  
• Preferred Background / Industry Experience

• Experience in lending or financial services, business-user support, and data reconciliation preferred.

Benefits

What We Offer

💰 Compensation in USD.

🏖️ Benefits include paid time off (PTO).

🌍 Work Environment: Fully remote work environment.

Ready to Apply?

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