Decision Support Data Scientist

HP PPS ASIA PACIFIC PTE. LTD.; Singapore; Presencial; SGD 90.000 - 120.000; Jornada completa

Descripción de la vacante

HP PPS Asia Pacific PTE. LTD. seeks a Decision Support Data Scientist to apply advanced analytics and decision science to complex supply chain planning problems in Singapore.

The role translates planning challenges into structured analytical problems, diagnoses performance drivers, evaluates scenarios and develops evidence-based recommendations to improve planning decisions. The position sits between planning decision science and technical data science, collaborating with planners, data

Formación

• Translate complex and sometimes ambiguous supply chain planning challenges into structured analytical problems.
• Define the decisions to be made, relevant objectives, constraints, assumptions, uncertainties and measures of success.
• Develop hypotheses and analytical approaches to investigate planning problems and identify underlying drivers.

Responsabilidades

• Translate complex and sometimes ambiguous supply chain planning challenges into structured analytical problems.
• Define the decisions to be made, relevant objectives, constraints, assumptions, uncertainties and measures of success.
• Develop hypotheses and analytical approaches to investigate planning problems and identify underlying drivers.
• Determine the appropriate analytical methodology based on the business problem rather than applying predefined tools or techniques.
• Work with planning stakeholders to understand operational context and ensure analytical approaches reflect real‑world business constraints.
• Analyse large and complex supply chain datasets using Python, SQL and statistical techniques.
• Conduct diagnostic and root‑cause analyses across demand, supply, inventory, forecast performance and planning processes.
• Identify relationships, patterns and drivers that explain planning performance and business outcomes.
• Establish analytical baselines and compare performance across products, markets, segments and time horizons.
• Apply appropriate statistical methods to distinguish meaningful business signals from normal operational variation.
• Develop clear analytical narratives that explain what happened, why it happened and what actions should be considered.
• Develop and evaluate planning scenarios to support decisions involving inventory, supply availability, demand uncertainty, cost, responsiveness and operational risk.
• Conduct what‑if, sensitivity and trade‑off analyses to evaluate alternative planning strategies.
• Quantify the expected benefits, risks and consequences associated with different courses of action.
• Evaluate decisions under uncertainty and identify assumptions or conditions that materially change the recommended action.
• Develop repeatable decision frameworks for recurring planning problems.
• Support planners and business leaders in understanding available options and making evidence‑based decisions.
• Translate analytical findings into clear and actionable recommendations.

Descripción del empleo

Job Description

JOB SUMMARY

HP is seeking a Decision Support Data Scientist to apply advanced analytics, statistical methods and decision science to complex supply chain planning problems. The role will focus on translating planning challenges into structured analytical problems, diagnosing performance drivers, evaluating scenarios and trade-offs, and developing evidence-based recommendations that improve planning decisions.

This is a hands‑on analytical role positioned between supply chain planning and technical data science. The successful candidate will work closely with Planning Decision Science leaders, planners, Data Scientists and Analytics Automation teams to turn data and analytical outputs into actionable business decisions.

The role is not primarily focused on production machine‑learning engineering or dashboard development. Instead, it requires strong quantitative problem‑solving skills and the ability to determine what the data means, evaluate alternative courses of action, quantify trade‑offs and recommend what the business should do.

The successful candidate should be comfortable working with ambiguous business problems, large datasets and imperfect information, and should be able to communicate analytical conclusions clearly to both technical and business stakeholders.

KEY RESPONSIBILITIES

1. 1. DECISION SCIENCE & PROBLEM FRAMING
   • - Translate complex and sometimes ambiguous supply chain planning challenges into structured analytical problems.
   • - Define the decisions to be made, relevant objectives, constraints, assumptions, uncertainties and measures of success.
   • - Develop hypotheses and analytical approaches to investigate planning problems and identify underlying drivers.
   • - Determine the appropriate analytical methodology based on the business problem rather than applying predefined tools or techniques.
   • - Work with planning stakeholders to understand operational context and ensure analytical approaches reflect real‑world business constraints.
2. 2. DIAGNOSTIC & QUANTITATIVE ANALYTICS
   • - Analyse large and complex supply chain datasets using Python, SQL and statistical techniques.
   • - Conduct diagnostic and root‑cause analyses across demand, supply, inventory, forecast performance and planning processes.
   • - Identify relationships, patterns and drivers that explain planning performance and business outcomes.
   • - Establish analytical baselines and compare performance across products, markets, segments and time horizons.
   • - Apply appropriate statistical methods to distinguish meaningful business signals from normal operational variation.
   • - Develop clear analytical narratives that explain what happened, why it happened and what actions should be considered.
3. 3. SCENARIO ANALYSIS & DE
   • - Develop and evaluate planning scenarios to support decisions involving inventory, supply availability, demand uncertainty, cost, responsiveness and operational risk.
   • - Conduct what‑if, sensitivity and trade‑off analyses to evaluate alternative planning strategies.
   • - Quantify the expected benefits, risks and consequences associated with different courses of action.
   • - Evaluate decisions under uncertainty and identify assumptions or conditions that materially change the recommended action.
   • - Develop repeatable decision frameworks for recurring planning problems.
   • - Support planners and business leaders in understanding available options and making evidence‑based decisions.
4. 4. RECOMMENDATIONS & BUSINESS IMPACT
   • - Translate analytical findings into clear and actionable recommendations.
   • - Develop recommendations based on quantitative evidence, business constraints, uncertainty and trade‑offs rather than reporting analytical results alone.
   • - Present scenarios, assumptions, alternatives, risks and expected outcomes clearly to business stakeholders.
   • - Work with Planning Decision Science leaders to challenge and refine recommendations before implementation.
   • - Track the outcomes of implemented recommendations and compare realised results against expected benefits.
   • - Use observed outcomes to improve future analytical approaches and decision frameworks.
5. 5. ANALYTICAL DE