{"id":2953309,"url":"https://alion.io/job/hp-decision-support-data-scientist","title":"Decision Support Data Scientist","company":{"id":2088298,"name":"HP","domain":"hewlett-packard.de","url":"https://alion.io/company/hewlett-packard-de","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":60,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":15,"computed_at":"2026-10-22T06:10:10Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":47000,"max_usd":96000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":27},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Git","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false}],"status":"live","first_seen_at":"2026-10-20T20:41:12Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-23T00:20:54Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Decision Support Data ScientistDescription -\nJOB SUMMARY\nHP is seeking a Decision Support Data Scientist to apply advanced analytics, statistical methods and decision science to complex supply chain planning problems.\nThe 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.\nThis 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.\nThe 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.\nThe 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.\nKEY RESPONSIBILITIES\n1. DECISION SCIENCE & PROBLEM FRAMING\nTranslate complex and sometimes ambiguous supply chain planning challenges into structured analytical problems.\nDefine the decisions to be made, relevant objectives, constraints, assumptions, uncertainties and measures of success.\nDevelop hypotheses and analytical approaches to investigate planning problems and identify underlying drivers.\nDetermine the appropriate analytical methodology based on the business problem rather than applying predefined tools or techniques.\nWork with planning stakeholders to understand operational context and ensure analytical approaches reflect real-world business constraints.\n2. DIAGNOSTIC & QUANTITATIVE ANALYTICS\nAnalyse large and complex supply chain datasets using Python, SQL and statistical techniques.\nConduct diagnostic and root-cause analyses across demand, supply, inventory, forecast performance and planning processes.\nIdentify relationships, patterns and drivers that explain planning performance and business outcomes.\nEstablish analytical baselines and compare performance across products, markets, segments and time horizons.\nApply appropriate statistical methods to distinguish meaningful business signals from normal operational variation.\nDevelop clear analytical narratives that explain what happened, why it happened and what actions should be considered.\n3. SCENARIO ANALYSIS & DECISION SUPPORT\nDevelop and evaluate planning scenarios to support decisions involving inventory, supply availability, demand uncertainty, cost, responsiveness and operational risk.\nConduct what-if, sensitivity and trade-off analyses to evaluate alternative planning strategies.\nQuantify the expected benefits, risks and consequences associated with different courses of action.\nEvaluate decisions under uncertainty and identify assumptions or conditions that materially change the recommended action.\nDevelop repeatable decision frameworks for recurring planning problems.\nSupport planners and business leaders in understanding available options and making evidence-based decisions.\n4. RECOMMENDATIONS & BUSINESS IMPACT\nTranslate analytical findings into clear and actionable recommendations.\nDevelop recommendations based on quantitative evidence, business constraints, uncertainty and trade-offs rather than reporting analytical results alone.\nPresent scenarios, assumptions, alternatives, risks and expected outcomes clearly to business stakeholders.\nWork with Planning Decision Science leaders to challenge and refine recommendations before implementation.\nTrack the outcomes of implemented recommendations and compare realised results against expected benefits.\nUse observed outcomes to improve future analytical approaches and decision frameworks.\n5. DATA SCIENCE & ADVANCED ANALYTICS COLLABORATION\nCollaborate with Data Science teams developing forecasting, optimisation, simulation and predictive models.\nTranslate planning and decision requirements into analytical and modelling requirements.\nUnderstand and critically evaluate model outputs, assumptions, limitations and business implications.\nConduct business validation of model recommendations against operational constraints and planning objectives.\nIdentify situations where predictive modelling, optimisation, simulation or other advanced analytical techniques could materially improve decision quality.\nIntegrate outputs from technical models into broader business recommendations and decision frameworks.\nPartner with Data Scientists on experimentation and analytical prototypes where appropriate.\n6. ANALYTICAL DEVELOPMENT\nDevelop analytical solutions using Python, SQL and other quantitative tools.\nBuild reusable analytical methodologies, scenario models and decision-support frameworks.\nPerform data preparation, feature analysis, statistical testing and exploratory modelling required to support planning decisions.\nPrototype analytical approaches before handing repeatable technical requirements to Data Science or Analytics Automation teams where appropriate.\nCollaborate with Analytics Automation teams to operationalise recurring analytical workflows into scalable tools, applications or automated processes.\nMaintain appropriate analytical documentation, assumptions and quality controls.\n7. CROSS-FUNCTIONAL COLLABORATION\nWork closely with regional and global supply chain planning stakeholders to understand business challenges and decision requirements.\nCollaborate across Planning Decision Science & Analytics, Data Science, Analytics Automation and Portfolio & Adoption teams.\nCommunicate complex quantitative findings clearly to technical and non-technical stakeholders.\nChallenge assumptions constructively and use evidence to influence planning decisions.\nSupport continuous improvement of planning processes, analytical capabilities and decision-making practices.\nEDUCATION & EXPERIENCE\nBachelor'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.\nTypically 4-7 years of relevant experience in data science, decision science, advanced analytics, operations research, supply chain analytics or another quantitative analytical function.\nDemonstrated experience independently analysing complex datasets and translating analytical findings into business recommendations.\nExperience structuring ambiguous business problems and determining appropriate analytical approaches.\nStrong quantitative and statistical problem-solving capabilities.\nDemonstrated ability to evaluate alternatives, quantify trade-offs and communicate recommendations to business stakeholders.\nTECHNICAL QUALIFICATIONS\nStrong Python skills for data manipulation, statistical analysis and analytical modelling.\nStrong SQL skills and experience working with large and complex datasets.\nExperience with Python analytical libraries such as Pandas, NumPy, SciPy, Scikit-learn or equivalent.\nStrong understanding of statistical analysis, hypothesis testing and quantitative modelling.\nExperience with scenario modelling, sensitivity analysis or simulation.\nUnderstanding of predictive modelling and machine-learning concepts.\nAbility to interpret and critically evaluate model outputs rather than treating model predictions as deterministic answers.\nExperience with data visualisation tools such as Power BI, Tableau or equivalent.\nFamiliarity with optimisation, operations research or mathematical modelling is advantageous.\nExperience with Git and modern analytical development practices is advantageous.\nSUPPLY CHAIN / BUSINESS QUALIFICATIONS\nExperience applying analytics to business or operational decision-making.\nSupply chain planning experience is strongly preferred, particularly within demand planning, supply planning, inventory management or supply-demand balancing.\nUnderstanding of common planning trade-offs involving inventory, availability, service, cost, responsiveness and risk.\nExperience within technology, electronics, semiconductor, manufacturing or another complex global supply chain environment is advantageous.\nFamiliarity with enterprise planning platforms such as SAP, Kinaxis, o9, Blue Yonder or equivalent is advantageous.\nKEY COMPETENCIES\nDecision Science\nStructured Problem Solving\nStatistical & Quantitative Analysis\nScenario & Sensitivity Analysis\nTrade-off Analysis\nData Science\nSupply Chain Analytics\nBusiness Acumen\nAnalytical Communication\nCritical Thinking\nStakeholder Management\nCross-Functional Collaboration\nLearning Agility\nROLE BOUNDARY\nThe Decision Support Data Scientist owns analytical problem solving and supports the development of business recommendations.\nThe role is expected to answer questions such as:\nWhat is happening?\nWhy is it happening?\nWhat decisions are available?\nWhat are the trade-offs between those decisions?\nHow do uncertainty and changing assumptions affect the decision?\nWhat does the analytical evidence suggest we should do?\nThe role may develop analytical models and prototypes where required, but is not primarily accountable for production machine-learning engineering, enterprise data pipelines or application development.\nData Science teams provide specialised modelling, optimisation and technical capabilities where deeper technical development is required.\nAnalytics Automation teams provide scalable applications, data pipelines and workflow automation.\nPlanning Decision Science leadership integrates analytical evidence, business context and technical outputs into recommendations and supports planner decision-making and adoption.\nIMPACT\nImproves the quality and speed of supply chain planning decisions through quantitative analysis.\nConverts complex datasets and analytical outputs into actionable planning recommendations.\nSupports evaluation of planning alternatives and trade-offs under uncertainty.\nProvides analytical depth to regional and global Planning Decision Science initiatives.\nBridges business planning problems and advanced technical capabilities.\nJob -\nData & Information TechnologySchedule -\nFull timeShift -\nNo shift premium (Singapore)Travel -\nRelocation -\nEqual Opportunity Employer (EEO)-\nHP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s).\nPlease be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence.\nFor more information, review HP’sEEO Policy or read about your rights as an applicant under the law here: “ Know Your Rights: Workplace Discrimination is Illegal \"","description_format":"text","description_chars":11452,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["Laptops","Printers & Scanners"],"lifecycle":[{"event":"open","at":"2026-10-20T20:41:12Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":1,"expected_fill_days":15,"reasons":["conf:0","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-10-22T06:10:10Z"},"pay":null,"html_url":"https://alion.io/job/hp-decision-support-data-scientist","json_url":"https://alion.io/job/hp-decision-support-data-scientist.json","meta":{"generated_at":"2026-10-23T01:42:40Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":2873,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-24T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2088298},"rest":"https://alion.io/mcp/rest/get_company?id=2088298"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fhp-decision-support-data-scientist"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fhp-decision-support-data-scientist"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fhp-decision-support-data-scientist"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/hp-decision-support-data-scientist\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fhp-decision-support-data-scientist"}]}