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Executive Decision Scientist Jobs (NOW HIRING)

Judge Group, Inc.

Data Scientist IV

Bridgewater, NJ · On-site

Job Title: Technical Process Manager - Decision Scientist cum Consultant/ Data Scientist Iv ... executive decision-making and create business impact. Key Responsibilities: 1. Product Mindset ...

Judge Group, Inc.

Data Scientist IV

Bridgewater, NJ · On-site

Data Scientist Iv Location- Basking Ridge, NJ (Hybrid) 8 days/month in office (typically 2 days a ... executive decision-making and create business impact. Key Responsibilities: 1. Product Mindset o ...

Instacart

Senior Marketing Decision Scientist II

OR · Remote

As a Senior Marketing Decision Scientist II, you will shape how we measure, forecast, and optimize ... Create executive-ready dashboards and narratives in tools like Looker or Mode that track KPIs ...

Instacart

Senior Marketing Decision Scientist II

As a Senior Marketing Decision Scientist II, you will shape how we measure, forecast, and optimize ... Create executive-ready dashboards and narratives in tools like Looker or Mode that track KPIs ...

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Executive Decision Scientist information

What are the key skills and qualifications needed to thrive as an Executive Decision Scientist, and why are they important?

To thrive as an Executive Decision Scientist, you need strong expertise in data analytics, statistical modeling, and business strategy, typically supported by an advanced degree in a quantitative field. Proficiency with tools such as Python, R, SQL, and data visualization platforms like Tableau, as well as experience with machine learning frameworks, is essential. Outstanding communication, critical thinking, and stakeholder management set top performers apart in this role. These skills are crucial for translating complex data insights into strategic business recommendations that drive executive decision-making.

What is a decision scientist's salary?

The average salary for a decision scientist typically ranges from $80,000 to $130,000 annually, depending on experience, location, and industry. Senior decision scientists or those with specialized skills in data analysis and machine learning can earn higher salaries, often exceeding $150,000. Compensation may also include bonuses and benefits related to data-driven roles.

Is 40 too late for data science?

For an Executive Decision Scientist or similar data science roles, starting a career at 40 is not too late. Many professionals transition into data science later in life, leveraging prior experience, and can succeed by developing relevant skills such as programming, statistics, and machine learning through online courses or certifications.

What are Executive Decision Scientists?

Executive Decision Scientists are professionals who use data analytics, quantitative modeling, and business strategy to support high-level decision-making within organizations. They combine expertise in data science, statistics, and business acumen to inform and guide executives on complex business challenges. Their work often involves analyzing large datasets, developing predictive models, and translating insights into actionable strategies that align with organizational goals. Executive Decision Scientists play a key role in driving data-driven transformation and ensuring that leadership decisions are supported by robust evidence.

How much do decision scientists make?

Decision scientists typically earn a median salary ranging from $80,000 to $130,000 annually, depending on experience, location, and industry. Senior roles or those with advanced skills in data analysis, machine learning, and programming can earn higher salaries, often exceeding $150,000.

How do Executive Decision Scientists typically collaborate with senior leadership to influence business strategy?

Executive Decision Scientists frequently work closely with senior executives, providing data-driven insights and recommendations that shape high-level business decisions. They translate complex analytical findings into actionable strategies, ensuring that leadership understands both the opportunities and risks. This role often involves regular presentations, cross-functional workshops, and advising on strategic initiatives, requiring strong communication skills and the ability to connect technical analysis with business objectives. Successful Executive Decision Scientists build trust by aligning their work with organizational goals and actively participating in strategic planning sessions.

What is the difference between Executive Decision Scientist vs Data Analyst?

AspectExecutive Decision ScientistData Analyst
Required CredentialsAdvanced degrees in data science, statistics, or related fields; experience with machine learningBachelor's or master's in data analysis, statistics, or related fields
Work EnvironmentStrategic, high-level decision-making teams; executive meetingsOperational teams; reporting and data visualization tasks
Employer & Industry UsageCorporate, finance, tech firms focusing on strategic insightsVarious industries; operational data analysis
Search & Comparison IntentUnderstanding strategic roles and qualificationsOperational data analysis and reporting

Executive Decision Scientists focus on high-level strategic insights and predictive modeling to inform executive decisions, often requiring advanced degrees and experience. Data Analysts handle operational data, generate reports, and support daily business functions. While both roles analyze data, the Executive Decision Scientist operates at a strategic level, whereas Data Analysts focus on tactical, operational tasks.

Which 3 jobs will survive AI?

For an Executive Decision Scientist, roles that require complex strategic thinking, creativity, and nuanced judgment are less likely to be fully automated by AI. These include executive leadership positions, high-level strategic planning, and roles involving ethical decision-making. Skills such as advanced data analysis, critical thinking, and domain expertise will remain valuable in adapting to AI-driven changes.
What cities are hiring for Executive Decision Scientist jobs? Cities with the most Executive Decision Scientist job openings:
What are the most commonly searched types of Decision Scientist jobs? The most popular types of Decision Scientist jobs are:
What states have the most Executive Decision Scientist jobs? States with the most job openings for Executive Decision Scientist jobs include:
Executive Decision Scientist jobs near you

Direct Client - Decision Scientist

ChaTeck Incorporated

Seattle, WA • On-site

Other

Posted 2 days ago

New

Job description

Role: Direct Client - Decision Scientist

About the Role

This Decision Scientist will partner with Digital Product Managers, Engineering, UX, and Operations to drive data-informed product decisions across Product Ordering experiences. This role will leverage advanced analytics, experimentation, AI-enabled insights, and business performance analysis to help improve customer experience, operational efficiency, and product outcomes.

The ideal candidate combines strong analytical capabilities with business acumen and the ability to translate complex data into actionable recommendations for product and business leaders.

Required Skills & Background

  • Experience with Azure: data lake storage, SQL Server, and legacy systems
  • Experience with Oracle; performing exploratory data analysis and cleansing, massaging, and aggregating data
  • Proficiency in Excel, SQL, SAS, R, Python, Tableau/Power BI, and experimental design platforms
  • Working knowledge of business processes and strong general business acumen
  • Experience providing analytic support (code documentation, data transformations, algorithms, etc.)
  • Ability to procure and manipulate large-scale, complex data from a variety of systems (AWS, Azure, Oracle, on-prem, web tools, etc.)
  • Ability to effectively present complex technical material to non-technical audiences in an approachable way

Technology Used

  • Microsoft Office Suite
  • Smartsheet

Nice to Have

  • Databricks experience

Top Skills

  • Strategic Analytics & Decision Support (4+ years) — translating complex data into actionable recommendations for product and business leaders
  • Communication (5+ years)
  • Attention to Detail (5+ years)

Core Responsibilities

Product Performance & Insights

  • Analyze product, operational, and customer experience performance to identify trends, root causes, opportunities, and risks.
  • Develop actionable recommendations that influence product prioritization, roadmap decisions, and feature optimization.
  • Anticipate stakeholder questions and proactively provide insights that support effective decision-making.
  • Monitor and communicate performance against key business, customer, and operational KPIs.

Strategic Analytics & Decision Support

  • Build models, analyses, forecasts, and scenario planning tools that inform strategic prioritization and investment decisions.
  • Partner with Product Managers to quantify business impact, define success metrics, and measure return on investment for product initiatives.
  • Support roadmap planning by assessing tradeoffs, sizing opportunities, and evaluating expected outcomes.

Experimentation & Product Measurement

  • Define measurement strategies for new products and capabilities.
  • Design and evaluate A/B tests, pilots, and experiments to validate hypotheses and guide product decisions.
  • Establish product health, adoption, engagement, and operational success metrics.
  • Create standardized measurement frameworks that can be applied consistently across products and channels.

Data Products, Dashboards & AI Enablement

  • Build scalable dashboards, AI-powered tools, and self-service analytics capabilities that enable Product Managers to independently assess product performance.
  • Identify opportunities to automate recurring analyses and reporting.
  • Partner with data engineering and analytics teams to improve data quality, accessibility, and reporting capabilities.
  • Drive enhancements to existing dashboards and data products based on evolving business needs.

Business Problem Solving

  • Lead cross-functional teams through complex and ambiguous business questions: defining key problems and opportunities, developing analytical hypotheses, designing research and measurement approaches, and synthesizing results into actionable recommendations.
  • Conduct inquiry-driven analysis to uncover emerging customer, operational, and business insights.

Executive Communication & Storytelling

  • Develop concise, executive-ready narratives that communicate business performance, product outcomes, risks, and recommendations.
  • Present findings and strategic insights to product leadership and senior executives.
  • Translate technical analyses into clear business implications and recommended actions.

Thought Leadership

  • Act as a trusted analytics partner across Coffeehouse Ordering and broader Digital Product teams.
  • Promote best practices in product measurement, experimentation, decision science, and AI-enabled analytics.
  • Bring an outside-in perspective on emerging analytics techniques, product measurement frameworks, and decision-support capabilities.

A Day in This Role

Product Analytics & Monitoring

  • Build and maintain product health scorecards and performance dashboards.
  • Create automated reporting and monitoring solutions.
  • Conduct recurring business reviews for product leaders.

AI & Self-Service Analytics

  • Build AI-enabled tools that allow Product Managers to self-service product performance questions, explore trends and anomalies, access KPI reporting, and generate insights and recommendations.

Product Measurement & Experimentation

  • Establish success criteria for new features and experiences.
  • Measure feature adoption, conversion, efficiency improvements, and customer outcomes.
  • Evaluate pilot performance and develop scaling recommendations.

Strategic Analysis

  • Customer behavior analysis
  • Operational efficiency analysis
  • Forecasting and scenario planning
  • Investment prioritization support

Candidate Requirements

Education

  • Bachelor's degree in a relevant field preferred

How Success Is Measured

Customer Experience

  • Order entry speed
  • Quantum Metric digital experience data
  • Error rate
  • Customer adoption & engagement

Checkout & Transaction Performance

  • Payment speed
  • Cart completion rate
  • Checkout success rate
  • Transaction failure rate

Store Operations

  • Order-to-window (OTW) time
  • Window time
  • Order accuracy
  • Throughput
  • Peak-hour performance

Product Health

  • Feature adoption
  • Feature utilization
  • Customer satisfaction signals

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