{"id":32780,"url":"https://alion.io/job/m3usa-data-scientist-decision-science-modelling","title":"Data Scientist – Decision Science & Modelling","company":{"id":50511,"name":"M3","domain":"m3.com","url":"https://alion.io/company/m3-com","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":36,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":["GB"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":67000,"max_usd":151000,"period":"year","method":"role_country_seniority_unknown","sample_n":159},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":".NET","optional":false},{"name":"ISO 27001","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Synthetic Data","optional":false},{"name":"C#","optional":true},{"name":"Digital Twin","optional":true}],"status":"live","first_seen_at":"2026-07-23T03:14:29Z","employer_posted_date":"2026-07-23","last_verified_at":"2026-10-18T02:52:26Z","board_verified":true,"closed_at":null,"days_open":87,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":87},"description":"About M3: A Japanese global leader in the provision of ground-breaking and innovative technological and research solutions to the healthcare industry. The M3 Group operates in the US, Asia, and Europe with over 5.8 million physician members globally via its physician websites which include mdlinx.com, m3.com, research.m3.com, Doctors.net.uk, medigate.net, and medlive.cn. M3 Inc. is a publicly traded company on the Tokyo Stock Exchange (jp:2413, NIKKEI 225) with subsidiaries in major markets including the US, UK, Japan, South Korea, and China, and in 2020 was ranked in Forbes’ Global 2000 list. The M3 Group provides services to healthcare and the life science industry. In addition to market research, these services include medical education, ethical drug promotion, clinical development, job recruitment, and clinic appointment services. M3 has offices in Japan, UK, France, Germany, Brazil, Sweden, China, USA, and South Korea, as well as India.\nDue to continued growth, M3 MR is seeking a Data Scientist - Decision Science & Modelling in London, UK.\nBusiness Unit Mission:\nM3 MR, part of M3 Inc., provides the most comprehensive and highest-quality healthcare market research recruitment, data collection, and insight support services globally. With proprietary access to healthcare professionals and patient communities across more than 70 countries, M3 MR partners with pharmaceutical, biotech, medical device, and market research agencies to deliver trusted, compliant, and decision-ready insights at speed and scale.\nM3 MR holds ISO 20252, ISO 27001, and ISO 27701 certifications, reflecting the group’s commitment to data quality, respondent integrity, information security, and operational excellence across quantitative and qualitative methodologies. The group combines deep healthcare expertise, advanced technology platforms, and global operational reach to support clients across the full research lifecycle.\n We are seeking a Data Scientist to design and deliver analytical models and decision-support systems that improve understanding, prediction and decision-making across the business.\nThe role focuses on building practical models of complex real-world systems, working with imperfect data, uncertainty and competing objectives to generate commercially valuable outcomes.\nResponsible for developing deployable analytical solutions in partnership with Engineering teams, while not owning production infrastructure or application development.\nKey Responsibilities:\nModel Development\nDesign, develop and maintain statistical, probabilistic and simulation-based models.\nTranslate complex business questions into tractable modelling problems.\nSelect appropriate modelling approaches based on the characteristics of the problem rather than methodological preference.\nDevelop prototypes and working solutions iteratively, refining approaches as new information becomes available.\nBuild analytical assets that can be reused as products, decision-support tools or operational capabilities.\nDesign and analyse experiments to evaluate interventions, operational changes and model effectiveness.\nInference & Uncertainty\nWork effectively with incomplete, imperfect and evolving datasets.\nDevelop approaches for estimating missing information and combining evidence from multiple sources.\nQuantify uncertainty and communicate appropriate confidence in model outputs.\nTest assumptions and identify limitations within modelling approaches.\nOptimisation & Decision Support\nDevelop frameworks that improve operational and commercial decision-making.\nEvaluate alternative actions, trade-offs and potential outcomes.\nSupport automation of appropriate decision processes through analytical models.\nDesign experiments and simulations that inform strategic and operational choices.\nValidation & Quality\nValidate models using appropriate testing, back-testing and comparison techniques.\nAssess sensitivity to assumptions and changing conditions.\nMonitor model performance over time and identify concept drift or degradation.\nMaintain high standards of analytical rigour and reproducibility.\nCollaboration & Communication\nPartner closely with Business Analysts, Engineering, Product and operational teams.\nExplain modelling approaches, assumptions and results clearly to non-technical audiences.\nDocument methodologies, limitations and recommendations in a practical and accessible way.\nContribute to a culture of experimentation and evidence-based decision-making.\nProductisation & Engineering Partnership\nDesign models and analytical approaches with operational deployment in mind.\nWork closely with Engineering teams to translate models into scalable production solutions - Data Science defines the models while Engineering owns production implementation.\nDefine model inputs, outputs, assumptions and performance requirements required for implementation.\nSupport the development of APIs, services or analytical components by providing technical guidance and validation.\nContribute to testing and acceptance of implemented solutions to ensure behaviour aligns with model expectations.\nHelp define monitoring, evaluation and retraining requirements where appropriate.\nPartner with Product, Engineering and Business teams to ensure analytical solutions deliver measurable business value.\n Essential\nStrong grounding in applied statistics, modelling, data science, operational research, economics, mathematics or a related quantitative discipline.\nExperience building models that support real-world decisions, products or operational processes.\nExperience working with uncertainty, incomplete information and imperfect datasets.\nAbility to move from loosely defined problems to practical analytical solutions.\nStrong problem decomposition and structured thinking skills.\nAbility to communicate technical concepts clearly to non-technical stakeholders.\nStrong coding skills in Python or R.\nExperience developing analytical solutions in code rather than primarily through spreadsheet-based analysis.\nExperience taking analytical models from prototype through to operational deployment.\nCore Technical Skills\nCandidates should demonstrate strength in several of the following areas:\nStatistical modelling\nProbabilistic modelling\nBayesian inference\nForecasting\nMachine learning\nSimulation modelling\nAgent-based modelling\nOptimisation techniques\nExperimental design\nSynthetic data generation\nDecision science\nScenario analysis\nAs important as experience with any individual technique is the ability to understand trade-offs and select the most appropriate approach for the problem being solved.\nExperience or familiarity with the following is desirable\nBuilding simulation or digital twin style systems.\nOptimisation, operational research or decision science techniques.\nWorking with survey, panel or market research data.\nApplying machine learning or AI techniques to business problems.\nModel monitoring, governance and validation practices.\nContributing to analytical products rather than one-off analyses.\nModern AI and generative AI approaches.\nWorking in multidisciplinary teams alongside software engineers and product teams.\nSoftware development lifecycles and productionisation of analytical solutions.\nDefining requirements and acceptance criteria for model implementation\n Employee Benefits:\n25 days annual leave\nParticipation in a company bonus scheme linked to personal and company performance\nGroup Life Cover 4x salary\nPension 4%/4% employee/employer contributions\nVitality after probation\nStaff discount scheme\nDiscounted gym 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