Decision Support Data Scientist

Decision Support Data Scientist

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. 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. 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. SCENARIO ANALYSIS & DECISION SUPPORT

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. 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. DATA SCIENCE & ADVANCED ANALYTICS COLLABORATION

Collaborate with Data Science teams developing forecasting, optimisation, simulation and predictive models.

Translate planning and decision requirements into analytical and modelling requirements.

Understand and critically evaluate model outputs, assumptions, limitations and business implications.

Conduct business validation of model recommendations against operational constraints and planning objectives.

Identify situations where predictive modelling, optimisation, simulation or other advanced analytical techniques could materially improve decision quality.

Integrate outputs from technical models into broader business recommendations and decision frameworks.

Partner with Data Scientists on experimentation and analytical prototypes where appropriate.

6. ANALYTICAL DEVELOPMENT

Develop analytical solutions using Python, SQL and other quantitative tools.

Build reusable analytical methodologies, scenario models and decision-support frameworks.

Perform data preparation, feature analysis, statistical testing and exploratory modelling required to support planning decisions.

Prototype analytical approaches before handing repeatable technical requirements to Data Science or Analytics Automation teams where appropriate.

Collaborate with Analytics Automation teams to operationalise recurring analytical workflows into scalable tools, applications or automated processes.

Maintain appropriate analytical documentation, assumptions and quality controls.

7. CROSS-FUNCTIONAL COLLABORATION

Work closely with regional and global supply chain planning stakeholders to understand business challenges and decision requirements.

Collaborate across Planning Decision Science & Analytics, Data Science, Analytics Automation and Portfolio & Adoption teams.

Communicate complex quantitative findings clearly to technical and non-technical stakeholders.

Challenge assumptions constructively and use evidence to influence planning decisions.

Support continuous improvement of planning processes, analytical capabilities and decision-making practices.

EDUCATION & EXPERIENCE

Bachelor's or Master's degree in Data Science, Statistics, Applied Mathematics, Operations Research, Industrial Engineering, Computer Science, Supply Chain Analytics, Engineering or a related quantitative discipline.

Typically 4–7 years of relevant experience in data science, decision science, advanced analytics, operations research, supply chain analytics or another quantitative analytical function.

Demonstrated experience independently analysing complex datasets and translating analytical findings into business recommendations.

Experience structuring ambiguous business problems and determining appropriate analytical approaches.

Strong quantitative and statistical problem-solving capabilities.

Demonstrated ability to evaluate alternatives, quantify trade-offs and communicate recommendations to business stakeholders.

TECHNICAL QUALIFICATIONS

Strong Python skills for data manipulation, statistical analysis and analytical modelling.

Strong SQL skills and experience working with large and complex datasets.

Experience with Python analytical libraries such as Pandas, NumPy, SciPy, Scikit-learn or equivalent.

Strong understanding of statistical analysis, hypothesis testing and quantitative modelling.

Experience with scenario modelling, sensitivity analysis or simulation.

Understanding of predictive modelling and machine-learning concepts.

Ability to interpret and critically evaluate model outputs rather than treating model predictions as deterministic answers.

Experience with data visualisation tools such as Power BI, Tableau or equivalent.

Familiarity with optimisation, operations res