Marketing Mix Modelling Data Scientist

City of London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Director, Data Science and Analytics

Data Scientist

Marketing Data Analyst - Hybrid, Impact & Insights

Marketing Data Analyst – Insights & Automation (Hybrid)

Marketing Data Analyst – Drive Growth & Insights

Marketing Data Analyst - Hybrid Insights & Automation

The Role:

An exciting opportunity for a Marketing Mixed Modelling technical expert working for a global agency. You will work closely with the Analytics Directors to manage resources, implement process improvements and provide mentorship within a tight knit account team consisting of analysts, media planners and strategists as they work to analyse data and deliver valuable and relevant insights. Through data-driven insights, you will have the opportunity to shape the strategic direction of a client's marketing strategy.

Responsibilities:

Client Relationship Management

Client Presentation: Present findings/insights as well as methodology/data approaches reliably to clients through a variety of different presentation opportunitiesTeam Operations

Collaboration: Connect across planning, investment, and technology teams to ensure holistic understanding of data
Management: Start your management career by having Associate, Sr. Associate and Associate Managers reporting into you and managing their workload and career developmentAudience Discovery & Strategy

Data Analysis: Become more seasoned in audience definition, creation and strategy using multiple data sources
Data Analysis Management: Manage your team to organise and analyse data and facilitate insight generation for which you will be responsible
Data Sources: Employ solid understanding of Audience data sources and how to best leverage them for each type of analysisMeasurement & Reporting

Measurement: Develop breadth of knowledge measurement strategy, frameworks and technology
Benchmarks & Goals: Set benchmarks and targets based on historical campaign data
Reporting & Optimization: Oversee and QA the development of reports and directing optimisation initiatives
Insights: Hone the ability to know what an insight is, and developing them for the campaignsData Strategy & Technology

Data Technology Utilisation: Manage your team to oversee automation and improve processes for efficiencies
Data Management: Be accountable for the maintenance and QA of data systems and processes used for reporting and on-going data analysis
Data Visualization: Develop advanced Dashboards in visualization tools such as Tableau/QlikView/Looker
Ad Operations: Apply advanced understanding of Ad Operations and QA procedures
AdTech/MarTech: Evaluate and compare data, ad and MarTech vendorsQualifications & Expertise Required:

Bachelor's degree in Statistics, Mathematics, Economics, Engineering, Information Management, Social Sciences or Business/Marketing related fields (advanced degree - MBA/MS - is preferred)
4-5 years of experience in a quantitative data driven field
A passion for digital marketing, research and analytics
Excellent communication and presentation skills
Ability to work well with others and work in cross functional teams
Ability to manage and prioritise a number of concurrent tasks
Ability to clearly explain complex technical ideas to multiple audiences both verbally and in writing
Comprehensive knowledge of ad technologies and research techniques (how they work, and how to troubleshoot)
Ability to move beyond descriptive analytics and employ more sophisticated techniques (predictive & prescriptive analytics)
Ability to set individual goals for analysts and measuring individual success/performance
Experience/familiarity in SAS, SPSS, R or other advanced analytics software packages
Experience/familiarity in ad-serving and web analytics tools (Google DFA, Atlas, Google Analytics, Omniture, etc.)
Experience/familiarity with concepts of database design and SQL
Experience/familiarity with syndicated research sources/tools (Gfk, MRI, Simmons, Scarborough, IMS, Nielsen, comScore)
Experience/familiarity with digital ad effectiveness research
Proficiency with Microsoft Excel and PowerPoint & a Data visualisation tool (Tableau)
Familiarity with web technologies including HTML and JavascriptDiversity, equity and inclusion are at the heart of what we value as an organisation. Boston Hale is an equal opportunities employer, and all qualified applicants will receive consideration for employment without regard to race, religion, sex, sexual orientation, age, disability or any other status protected by law

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.