Decision Support Data Scientist

About the role

HP is looking for a Decision Support Data Scientist in Singapore who applies advanced analytics, statistical methods and decision science to complex supply chain planning problems. The role sits between supply chain planning and technical data science, turning planning challenges into structured analytical problems, diagnosing performance drivers, evaluating scenarios and trade-offs, and recommending evidence-based actions. It works closely with Planning Decision Science leaders, planners, Data Scientists and Analytics Automation teams.

Key responsibilities

• Translate ambiguous supply chain planning challenges into structured analytical problems, defining objectives, constraints, assumptions and measures of success
• Analyse large datasets with Python and SQL, run diagnostic and root-cause analyses, and build analytical baselines
• Develop and evaluate planning scenarios, including what-if, sensitivity and trade-off analyses
• Turn findings into clear, actionable recommendations and track their outcomes
• Collaborate with Data Science teams on forecasting, optimisation, simulation and predictive models, validating model outputs against business constraints
• Build reusable analytical methodologies, scenario models and decision-support frameworks, and work with Analytics Automation teams to automate recurring workflows
• Communicate quantitative findings clearly to technical and non-technical stakeholders

Required profile

• Bachelor's or Master's degree in a quantitative discipline such as data science, statistics, operations research, industrial engineering or computer science
• Typically 4-7 years of experience in data science, decision science, advanced analytics, operations research or supply chain analytics
• Experience independently analysing complex datasets and turning findings into business recommendations
• Ability to structure ambiguous problems, quantify trade-offs and communicate recommendations

Required skills

• Strong Python and SQL, with libraries such as Pandas, NumPy, SciPy or Scikit-learn
• Statistical analysis, hypothesis testing and quantitative modelling
• Scenario modelling, sensitivity analysis or simulation
• Data visualisation tools such as Power BI or Tableau
• Familiarity with optimisation, operations research or mathematical modelling is advantageous