UK

United Kingdom · Operations · Lead (8-12 years)

Lead Operations Research Scientist

Here is the whole job, in plain words. What it is, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

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  • Experience bandLead (8-12 years)
  • Direct reports3-8 reports
  • Reports toDirector of Operations Research
  • UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Principal OR Analyst · Operations Optimisation Lead · Senior Modelling Specialist · OR Solutions Architect

Built on 43,079 real UK job descriptions we analysed · grounded in what real qualifications cover

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

ATHE Level 6 Award in Security Risk Management Consultancy ATHE Ltd · Level 6 · covers 3 of 15 standards
IRM's Level 5 International Certificate in Financial Services Risk Management Institute of Risk Management · Level 5 · covers 2 of 15 standards
CMI Level 5 NVQ Diploma in Management and Leadership Chartered Management Institute · Level 5 · covers 1 of 15 standards
ProQual Level 4 NVQ Diploma in Management ProQual Awarding Body · Level 4 · covers 7 of 15 standards
OCNLR Level 4 Certificate in Business Administration OCN London · Level 4 · covers 3 of 15 standards

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

organisational Resilience ATHE Level 6 Award in Security Risk Management Consultancy
Business continuity and the disaster recovery process SFJ Awards Level 5 Certificate for the Security Risk Management Practitioner (RQF)
Resilience IRM's Level 5 International Certificate in Financial Services Risk Management
Manage business risk CMI Level 5 NVQ Diploma in Management and Leadership
Implement and maintain business continuity plans and processes ProQual Level 4 NVQ Diploma in Management
Business Continuity Planning OCNLR Level 4 Certificate in Business Administration

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
AI Governance Fluency using AI responsibly
Automation Fluency building AI into how work runs
Change Fluency taking people through change
Your Plan Illustration

Built for Lead Operations Research Scientist

6 units that map to this job, from the qualifications that cover it.

  1. organisational Resilience ATHE Ltd · covers 3 of 15 standards Level 6
  2. Business continuity and the disaster recovery process SFJ Awards · covers 2 of 15 standards Level 5
  3. Resilience Institute of Risk Management · covers 2 of 15 standards Level 5
  4. Manage business risk Chartered Management Institute · covers 1 of 15 standards Level 5
  5. Implement and maintain business continuity plans and processes ProQual Awarding Body · covers 7 of 15 standards Level 4
  6. Business Continuity Planning OCN London · covers 3 of 15 standards Level 4
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Lead Operations Research Scientist?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

The new skills this role is being asked for

  • Prompt Engineering & LLM Integration
  • Data Mesh & Data Product Thinking
Why each one is rising

The future skills this job leans on

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

What happens when you start

Next, a free account. We build your Lead Operations Research Scientist plan as you set up, starting with the free Automation Fluency module. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

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Keep this read. One email with what AI is changing in this job, the new skills it asks for and where to start free. Nothing else follows unless you confirm your address.

3What this role really is

You design and build complex optimisation models that change how operations are run. You architect solutions and lead a small team to deliver them, working across the fine detail and the bigger picture, while managing expectations across the business.

4What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

Gurobi / CPLEX Expert

Solving large-scale linear, integer, and mixed-integer programming problems for network optimisation, scheduling, and resource allocation. Interpreting advanced solver outputs like duals and reduced costs.

Python (SciPy, scikit-learn, PuLP/Gurobipy) Expert

Building custom statistical models, machine learning algorithms for forecasting, and integrating optimisation solvers. You'll be writing production-ready code and teaching best practices to your team.

AnyLogic / Simio (Professional) Advanced

Designing, building, and running complex discrete-event and agent-based simulations of operational processes (e.g., warehouse operations, supply chain flows) to test scenarios and identify bottlenecks.

SQL (PostgreSQL/MS SQL Server) Advanced

Complex data extraction, transformation, and loading from various operational databases. Writing efficient queries, stored procedures, and understanding database schemas to feed your models.

AWS (S3, EC2, Lambda) / Azure (Blob, VMs, Functions) Intermediate

Using cloud compute resources for running intensive optimisation models, managing data storage, and understanding how your models can be deployed and scaled in a cloud environment. You'll be working with our IT teams on this.

Tableau / Power BI (Desktop & Server) Advanced

Creating compelling, interactive dashboards and visualisations to communicate model inputs, outputs, and business insights to a wide range of stakeholders, from operational managers to senior leadership.

5What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choiceComing in Where you are nowThe step above
Project Scope & Methodology Follows defined methodology, escalates scope changes. Chooses appropriate methodology for routine problems, proposes scope adjustments. Designs complex solution architectures, defines project scope, makes technical trade-offs within workstream.
Resource Allocation (Team) Assigned tasks by supervisor. Manages own time for assigned tasks. Allocates own time across multiple workstreams. May informally guide junior colleagues.
Budget Authority None. Recommends small purchases (e.g., software licences under £1K). Recommends project budgets up to £5K. Consults on larger spending.
Stakeholder Communication Communicates findings to immediate team. Presents analysis to internal clients (e.g., department managers). Leads discussions with cross-functional leads, presents recommendations to senior managers.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

Project ROI Delivered
The documented financial return (cost savings or revenue uplift) generated by your team's optimisation projects, compared to the project's cost.
Target · Achieve at least 3x ROI on all major projects within 12 months of deployment.

Your team's new routing model for our delivery fleet saved £1.5M in fuel and labour costs this year, against a project cost of £300K, giving a 5x ROI.

Model Adoption Rate
The percentage of operational teams or processes that consistently use the models and recommendations your team has built.
Target · Maintain >80% adoption rate for critical operational models.

Our new inventory optimisation model is being used by 9 out of 10 regional warehouse managers, showing an 85% adoption rate.

Forecast Accuracy (MAPE)
The Mean Absolute Percentage Error (MAPE) for key operational demand forecasts your models generate, particularly for A-class items (high value/volume).
Target · Keep MAPE below 15% for all A-class item forecasts.

Last month's forecast for our top 50 products had an average MAPE of 12%, well within our target.

Team Productivity
The average number of significant projects or model enhancements delivered per analyst in your team per quarter.
Target · Average 1.5 major deliverables per analyst per quarter.

Your team of 4 analysts completed 6 major model updates and 2 new project builds last quarter, hitting the target.

Strategic Influence
How often your team's insights and models directly inform significant operational or business-wide strategic decisions.
  • You're regularly invited to strategic planning meetings. Senior leaders actively seek your team's input before making major investments (e.g., new warehouse locations, technology upgrades). Your models are cited in board-level presentations.
Team Development & Mentorship
The growth and capability development of your direct reports, and how effectively you're building their skills.
  • Your team members are taking on more complex tasks independently. They're actively contributing to solution design. You're receiving positive feedback from your reports about your guidance and support. Junior analysts are visibly progressing in their careers under your wing.
Solution Robustness & Scalability
The ability of the models and solutions you design to handle real-world variability, adapt to changing business needs, and scale with growth.
  • Models don't break down when faced with unexpected data or scenarios. They're built with future expansion in mind. Operational teams trust the models to perform reliably under pressure. You're proactively identifying and addressing potential future limitations.
Cross-Functional Collaboration
How effectively you work with other teams (e.g., Logistics, Supply Chain, IT, Finance) to ensure models are integrated and adopted.
  • You're seen as a trusted partner by other department heads. You're able to get different teams to agree on data definitions and project scope. There are clear communication channels and shared understanding across project teams. You're the one bringing people together to solve problems.

7Would you like it

Both sides. What people enjoy, and what grinds them down.

What people enjoy
Solving Complex, Real-World Problems

You'll be tackling genuinely hard operational puzzles, like optimising our entire logistics network or figuring out the best way to staff our customer service centres given fluctuating demand. These aren't textbook problems; they're messy, full of constraints, and have real financial implications.

Spending a week deep-diving into our warehouse picking process, identifying the root causes of inefficiency, and then designing a simulation model to test new layouts and strategies.

Seeing Tangible Business Impact

Your work won't just sit in a report. You'll see your models deployed, your recommendations implemented, and then measure the actual cost savings or efficiency gains. There's a direct line between your analysis and significant improvements in how the business runs.

Presenting to the Head of Logistics that your new routing algorithm is projected to save £1.2M annually, and then seeing that saving hit the P&L a few months later.

Leading & Developing a Team

You'll be responsible for guiding and growing a small team of talented analysts. This means mentoring them through technical challenges, helping them refine their problem-solving skills, and fostering a collaborative environment where everyone learns from each other.

Running a weekly code review session with your team, not just to spot errors, but to teach best practices and discuss alternative approaches to model building.

What frustrates people
  • The 'data is a lie' problem: The numbers in the system often don't reflect reality, making your models inherently flawed from the start.
  • Management by gut feel: Senior leaders overriding data-driven recommendations with anecdotal evidence or 'how we've always done it'.
  • Black box mistrust: Operational teams being sceptical of complex models they don't fully understand, making adoption a constant uphill battle.
  • The 'can you just...' request: Being asked to 'quickly run the numbers' for a major strategic decision, completely underestimating the weeks of work involved.
  • Implementation challenges: The model is perfect, but human factors, resistance to change, or unwritten tribal knowledge make it hard to deploy effectively.
  • Solving yesterday's problem: By the time your model is perfected, the business has moved on, rendering your solution obsolete.
What this role does not give you
  • A perfectly clean, ready-to-use dataset for every project.
  • Guaranteed, immediate adoption of every model you build.
  • A purely academic environment where theoretical elegance trumps practical implementation.
  • A role where you can avoid managing people or dealing with stakeholder politics.
  • A predictable, unchanging work schedule; urgent operational issues will pop up.

8Who you work with

Your role directly impacts our operational efficiency and cost base. Your models will influence major capital expenditure decisions, staffing levels, and how we deliver services across the entire business. Essentially, you're shaping how we operate, making sure we're doing things as smartly and effectively as possible. You'll be the one providing the data-driven answers to some of our toughest operational questions.

Inside the business
  • Director of Operations Research (your line manager)
  • Head of Logistics (for transport and warehousing optimisation)
  • Head of Supply Chain (for inventory and network design projects)
  • Product Managers (when integrating OR models into our tech platforms)
  • Finance Business Partners (to validate cost savings and ROI)
  • Senior Leadership Team (for project updates and strategic input)
Outside the business
  • Software vendors (for optimisation solvers or simulation tools)
  • External consultants (occasionally, for specialist expertise)
  • Academic partners (for research collaborations or novel approaches)

9What you need before you start

Not a wish list. The things you would be expected to already have.

  • A proven track record of successfully leading and delivering complex Operations Research projects from problem definition to implementation.
  • Demonstrable experience managing and mentoring a small team of analysts, including providing technical guidance and fostering their development.
  • Expert-level proficiency in at least one commercial optimisation solver (e.g., Gurobi, CPLEX) and Python for model building and data analysis.
  • Strong experience in designing and building discrete-event simulation models for operational systems.
  • The ability to effectively communicate highly technical concepts and business implications to both technical and non-technical audiences, including senior leadership.
  • A deep understanding of core operational processes within supply chain, logistics, or manufacturing.

10What to practise next

Where the job is going, and what to do about it starting this week.

Cloud-Native OR Deployment

Moving from running models on local machines to deploying them as scalable, robust services in the cloud. This means your models can run faster, handle more data, and be integrated seamlessly into our operational systems, providing real-time recommendations.

Containerisation (Docker) · Orchestration (Kubernetes) · Serverless Functions (AWS Lambda/Azure Functions) · API Development · Cloud Cost Optimisation

  • This quarter: Take an online course on Docker and containerisation, focusing on how to package a Python OR model.
  • Next quarter: Work with our IT/Cloud team to deploy a simple OR model as a containerised service in our cloud environment.
  • Month 6: Explore how to expose your model via a REST API for consumption by other applications.
  • Month 9: Begin to consider cost implications when designing cloud-native OR solutions.

Quick win: Containerise one of your existing Python OR models using Docker. It's a foundational step that immediately improves portability.

Reinforcement Learning for Operations

While traditional OR excels at static optimisation, Reinforcement Learning (RL) offers a powerful approach for dynamic, sequential decision-making in complex environments (e.g., real-time traffic management, dynamic pricing, complex scheduling). It's a natural evolution for problems where the system state changes rapidly.

Markov Decision Processes (MDPs) · Agents & Environments · Reward Functions · Exploration vs. Exploitation · Common RL Algorithms (e.g., Q-learning, Policy Gradients)

  • This quarter: Complete an introductory online course on Reinforcement Learning (e.g., from Coursera, Udacity).
  • Next quarter: Identify a suitable small-scale operational problem (e.g., a simple inventory control scenario) and attempt to solve it using an RL library in Python (e.g., Gymnasium, Stable Baselines).
  • Month 6: Present your findings and potential applications of RL to your team, sparking discussions on its relevance to our operations.
  • Month 9: Collaborate with a data scientist or ML engineer on an RL pilot project if an opportunity arises.

Quick win: Read a few case studies on how companies are already using Reinforcement Learning in operations. It will open your eyes to the possibilities.

11Staying current once you are in

What people here do to keep up
  • Regularly attend industry conferences (e.g., OR Society, INFORMS) to stay updated on the latest research and network with peers.
  • Actively participate in online OR communities and forums, contributing to discussions and learning from others' experiences.
  • Dedicate time each quarter to learning a new programming language feature, optimisation technique, or cloud service relevant to your role.
  • Mentor junior colleagues formally and informally, sharing your knowledge and helping them grow.
  • Seek out opportunities to present your team's work internally to different departments, honing your communication and influence skills.

12The rising capability

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Prompt Engineering & LLM Integration

Frankly, competitors are already using large language models (LLMs) to draft reports in minutes that used to take hours, or to quickly prototype heuristic ideas. Analysts who master this will outproduce their peers significantly. Your value will shift to validating and interpreting AI outputs, and knowing when not to trust them.

  • Context Windows & Token Limits
  • Temperature Settings
  • RAG (Retrieval Augmented Generation)
  • Output Validation & Hallucination Detection
  • Prompt Chaining & Agentic Workflows

Data Mesh & Data Product Thinking

As our data ecosystem grows, we're moving away from centralised data lakes towards a 'data mesh' approach, where data is treated as a product owned by domain teams. This means you'll need to understand how to 'consume' data products from other operational domains and how to 'publish' your model outputs as reliable data products for others.

  • Domain-Oriented Data Ownership
  • Data as a Product
  • Self-Serve Data Platforms
  • Federated Governance
  • Data Contracts

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Linear & Integer Programming
  • Stochastic Modeling & Queuing Theory
  • Discrete-Event Simulation
  • Heuristics & Metaheuristics
  • Demand Forecasting & Time Series Analysis
  • Inventory Theory & Control

14The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

  1. 1

    Senior Operations Research Analyst (Internal Promotion)

    3-5 years as a Senior Analyst

    Skills to master

    • End-to-end ownership of complex workstreams, informal mentorship, strong stakeholder management for project-level decisions, deep specialisation in one or two OR methodologies.

    You're ready to move on when

    • Consistently delivering high-impact projects with minimal supervision.
    • Proactively identifying new OR opportunities within Operations.
    • Being the 'go-to' person for a specific technical area or problem type.
    • Successfully guiding junior team members on technical challenges.
  2. 2

    OR Consultant (from external)

    8-12 years in OR consulting

    Skills to master

    • Rapid problem framing, client relationship management, delivering solutions under tight deadlines, ability to adapt to diverse industry contexts, strong presentation skills.

    You're ready to move on when

    • A portfolio of successful OR projects across different clients/industries.
    • Experience leading project teams and managing client expectations.
    • Proven ability to translate complex OR into actionable business recommendations.
    • Strong track record of business development or project acquisition.
  3. 3

    Data Scientist / Machine Learning Engineer (from external)

    8-12 years in DS/ML with strong OR focus

    Skills to master

    • Deep expertise in ML algorithms, robust software engineering practices, experience with large-scale data processing, a strong understanding of optimisation principles and their application.

    You're ready to move on when

    • Experience building and deploying ML models that directly impact operational decisions.
    • A solid understanding of mathematical optimisation and its relevance to business problems.
    • Strong programming skills and experience with cloud platforms for model deployment.
    • A genuine interest in applying ML to solve complex operational challenges.

15Where this role leads

The long view: From here you could move into leading larger teams, become a specialist technical expert, or help shape the strategic direction of a business unit.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Lead Operations Research Scientist is actually changing.

Where it leads

Roles this leads to

How you would learn this

One to one, on the work you already do

A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.

A lesson from organisational Resilience, worked against Lead Operations Research Scientist.

organisational Resilience Level 6

Applied to your work in Lead Operations Research Scientist

This unit aims to provide learners with a comprehensive understanding of Organisational Resilience capability and its key components, including adaptability, agility, and robustness. Learners will explore the principles of criticality management, impact analysis, and the role of human factors in organisational resilience, enabling them to mitigate the effects of disruptions.

How the thinking builds
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 6 Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

See Your Progress Grow Illustration
Lead Operations Research Scientist
  • Linear & Integer Programming
  • Stochastic Modeling & Queuing Theory
  • Discrete-Event Simulation
  • Heuristics & Metaheuristics
  • Demand Forecasting & Time Series Analysis
  • Inventory Theory & Control
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

16The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Lead Operations Research Scientist is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Manager, Operations Research (L5)

    3-5 years in the Lead role

    This is a direct management path, moving from leading projects and a small team to managing the entire OR function. You'll be accountable for the team's overall strategy, budget, and business impact.

    • Vendor Management Negotiating contracts and managing relationships with external software providers and consultants.
    • Talent Acquisition & Development Building out the OR team through hiring, onboarding, and career development programmes.
    • P&L Impact Ownership Directly accountable for the documented financial impact of the entire OR function.
    • Cross-Departmental Strategy Integrating OR strategy with broader Operations, IT, and business unit strategies.

  2. Principal Operations Research Scientist (L5 - Individual Contributor)

    3-5 years in the Lead role

    This is a deep technical expert path. You'll become a recognised authority in a specific area of Operations Research, tackling the most complex, ambiguous, and novel problems without direct people management responsibilities. You'll mentor other Leads and shape the technical direction of the function.

    • Novel Algorithm Design Inventing and implementing entirely new optimisation algorithms or heuristics for previously unsolved problems.
    • Complex System Modelling Architecting and building OR solutions for enterprise-level, highly interconnected operational systems.
    • Technical Governance Setting standards for model quality, code robustness, and documentation across the OR team.
    • Strategic Technical Consulting Acting as an internal consultant for the most challenging technical OR problems across the business.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a big chunk of your day as a Lead Operations Research Scientist is spent on tasks that, while necessary, aren't always the most stimulating. Think about the endless data cleaning, the initial drafts of documentation, or even just brainstorming new heuristic approaches. What if you could offload a significant portion of that to intelligent assistants?

At Zavmo, we're not just talking about AI; we're actively embedding it into our daily workflows. For a Lead Operations Research Scientist, this means using AI to handle the grunt work, freeing you up to focus on the truly strategic, high-impact problem-solving and team leadership that only you can do. We believe in augmenting human intelligence, not replacing it.

Automated Data Cleansing & Prep

Imagine feeding messy data from our old ERP system into an AI tool that automatically spots anomalies, suggests cleaning rules, and even drafts the SQL queries to fix them. What used to take days of tedious manual work can now be done in hours, leaving you more time for actual model building and validation. You'll spend more time reviewing, less time scrubbing.

Enhanced Forecasting with ML

Beyond traditional time-series models, use advanced ML algorithms (like XGBoost or neural networks) that automatically pull in and weigh external factors – think weather patterns, economic indicators, or competitor promotions – to create far more accurate and robust demand forecasts. This means fewer stockouts, less waste, and happier customers, all with less manual tweaking from your team.

Accelerated Heuristic Design

Stuck on a particularly nasty NP-hard problem? Use generative AI as your brainstorming partner. Ask it to 'suggest novel heuristic approaches for a multi-depot vehicle routing problem with time windows' or 'provide Python starter code for a Tabu Search algorithm'. This dramatically speeds up your R&D phase, helping your team explore more solutions, faster.

Model Documentation & Stakeholder Comms

Let AI handle the initial drafts of your technical documentation, explaining the objective function, constraints, and key assumptions of your latest model. You can also use it to draft compelling, easy-to-understand emails for operational stakeholders, translating complex model outputs into clear business implications. This ensures your brilliant work is actually understood and adopted, without you spending hours on prose.

Common questions

Common questions

How do you become a Lead Operations Research Scientist?

Common routes in include Senior Operations Research Analyst (Internal Promotion) (3-5 years as a Senior Analyst), OR Consultant (from external) (8-12 years in OR consulting) and Data Scientist / Machine Learning Engineer (from external) (8-12 years in DS/ML with strong OR focus). Times vary with prior experience.

Where can a Lead Operations Research Scientist progress to?

This role can lead on to Manager, Operations Research (3-5 years in the Lead role) and Principal Operations Research Scientist (3-5 years in the Lead role), depending on the skills you build.

What new skills matter most for a Lead Operations Research Scientist?

Increasingly, Prompt Engineering & LLM Integration and Data Mesh & Data Product Thinking. These are the skills our record of this role lists as growing.

Free tools

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Three free tools, no account needed. Each one ends with a role, a level and a way in.

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17Where to go from here

Other roles at Level 5

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.

 More jobs in operations

Stay in the field you know and move sideways rather than up.
If you leave this industry

The skills built in this role transfer across industries. You could move into senior operations research roles in retail, finance, healthcare or government. Modelling complex systems and driving decisions from data are valued in most sectors.

This role profile is © 2026 Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Start my free Automation Fluency module What happens next: a free account, then we build your plan as you set up. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.