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About the Role

Design and deliver analytical models and decision-support systems to improve understanding, prediction, and decision-making across the business. The role focuses on building practical models of complex real-world systems, working with imperfect data, uncertainty, and competing objectives to generate commercially valuable outcomes. Responsible for developing deployable analytical solutions in partnership with Engineering teams.

Responsibilities

Design, develop, and maintain statistical, probabilistic, and simulation-based models.
Translate complex business questions into tractable modelling problems.
Select appropriate modelling approaches based on problem characteristics.
Develop prototypes and working solutions iteratively.
Build reusable analytical assets.
Design and analyze experiments to evaluate interventions, operational changes, and model effectiveness.
Develop approaches for estimating missing information and combining evidence from multiple sources.
Quantify uncertainty and communicate confidence in model outputs.
Test assumptions and identify limitations within modelling approaches.
Develop frameworks that improve operational and commercial decision-making.
Evaluate alternative actions, trade-offs, and potential outcomes.
Support automation of decision processes through analytical models.
Design experiments and simulations that inform strategic and operational choices.
Validate models using appropriate testing, back-testing, and comparison techniques.
Assess sensitivity to assumptions and changing conditions.
Monitor model performance over time and identify concept drift or degradation.
Maintain high standards of analytical rigour and reproducibility.
Partner closely with Business Analysts, Engineering, Product, and operational teams.
Explain modelling approaches, assumptions, and results clearly to non-technical audiences.
Document methodologies, limitations, and recommendations.
Contribute to a culture of experimentation and evidence-based decision-making.
Design models and analytical approaches with operational deployment in mind.
Work closely with Engineering teams to translate models into scalable production solutions.
Define model inputs, outputs, assumptions, and performance requirements for implementation.
Support the development of APIs, services, or analytical components by providing technical guidance and validation.
Contribute to testing and acceptance of implemented solutions.
Help define monitoring, evaluation, and retraining requirements.
Partner with Product, Engineering, and Business teams to ensure analytical solutions deliver measurable business value.

Requirements

Experience building models that support real-world decisions, products, or operational processes.
Experience working with uncertainty, incomplete information, and imperfect datasets.
Ability to move from loosely defined problems to practical analytical solutions.
Strong problem decomposition and structured thinking skills.
Ability to communicate technical concepts clearly to non-technical stakeholders.
Strong coding skills in Python or R.
Experience developing analytical solutions in code rather than primarily through spreadsheet-based analysis.
Experience taking analytical models from prototype through to operational deployment.
Building simulation or digital twin style systems is desirable.
Optimisation, operational research, or decision science techniques are desirable.
Working with survey, panel, or market research data is desirable.
Applying machine learning or AI techniques to business problems is desirable.
Model monitoring, governance, and validation practices are desirable.
Contributing to analytical products rather than one-off analyses is desirable.
Modern AI and generative AI approaches are desirable.
Working in multidisciplinary teams alongside software engineers and product teams is desirable.
Software development lifecycles and productionisation of analytical solutions are desirable.
Defining requirements and acceptance criteria for model implementation is desirable.

Skills

Applied statistics
Modelling
Data science
Operational research
Economics
Mathematics
Python
R
Statistical modelling
Probabilistic modelling
Bayesian inference
Forecasting
Machine learning
Simulation modelling
Agent-based modelling
Optimisation techniques
Experimental design
Synthetic data generation
Decision science
Scenario analysis

Location

London, UK

Work Type

Remote

Experience Level

Mid-level

Education Level

Degree in applied statistics, modelling, data science, operational research, economics, mathematics or a related quantitative discipline

Benefits

25 days annual leave
Participation in a company bonus scheme linked to personal and company performance
Group Life Cover 4x salary
Pension 4%/4% employee/employer contributions
Vitality after probation
Staff discount scheme
Discounted gym membership

About the Company

A Japanese global leader in providing 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.
M3 Inc. is a publicly traded company on the Tokyo Stock Exchange (jp:2413, NIKKEI 225).
M3 Group provides services to healthcare and the life science industry, including market research, 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.
M3 MR, part of M3 Inc., provides comprehensive and high-quality healthcare market research recruitment, data collection, and insight support services globally.
M3 MR partners with pharmaceutical, biotech, medical device, and market research agencies.
M3 MR holds ISO 20252, ISO 27001, and ISO 27701 certifications.
The group combines deep healthcare expertise, advanced technology platforms, and global operational reach.