Marketing Mix Modelling Data Scientist

City of London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist - Measurement Specialist

Data Scientist - Measurement Specialist

Customer Campaign Data Analyst - B2C

Data Analyst

Finance Data Analyst

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.

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.