MarketCast
Data Scientist II - Product
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Data Scientist II - Product Team
London, UK, Reading, UK
Role Impact
As our Data Scientist II - Product, you'll be a senior contributor and technical lead in our Data Science Product team. The Data Scientist II - Product takes ownership of the design, build and production operation of the data science products that underpin MarketCast's media and entertainment analytics. Operating at the intersection of quantitative market research and statistical modelling, this position leads solutions to moderately complex product problems, deploys and monitors models in production, and drives improvements to how the team works. Bayesian statistics is a core methodology for the team, and this role is expected to set the standard for Bayesian model design, validation and communication across products. On a daily basis, the data scientist defines success criteria, manages projects with multiple stakeholders across the UK, US and India, and guides more junior team members in modelling and deployment. By delivering scalable, well-engineered products and raising the statistical capability of the team, this individual directly increases the value and reliability of the organisation's analytics.
We're Looking For
Solution Leadership
- The individual takes the lead on directing, defining and implementing solutions to moderately complex product problems, effectively defining clear success criteria. This involves breaking ambiguous requirements into a technical roadmap, choosing between Bayesian, frequentist and machine learning approaches on their merits, and owning delivery end to end. Clear success criteria keep product development focused on business benefit rather than technical novelty. Success is measured by solutions delivered to agreed criteria with limited senior intervention.
Bayesian Modelling Leadership
- The role designs, builds and validates production-grade Bayesian models, including hierarchical and multilevel models, Bayesian time series and forecasting, and probabilistic models of audience behaviour, using frameworks such as PyMC, Stan or NumPyro. The data scientist sets team standards for prior specification, convergence diagnostics, posterior predictive checking and model comparison, and finds scalable inference approaches such as variational methods or reparameterisation where full MCMC is impractical at product scale. Rigorous Bayesian modelling is what allows the team to deliver calibrated uncertainty and robust estimates from sparse or noisy media data. Success is demonstrated by models that are trusted in production and by consistent Bayesian practice across the team.
Production Pipeline Ownership
- The individual owns the design, development and maintenance of production modelling pipelines and products, ensuring they are robust, scalable and well documented. This is executed through sound software engineering practices including version control, testing and code review, using Polars or pandas as appropriate for performance, and working with the India-based Data Engineering team on AWS. Well-engineered pipelines allow products to scale to new clients and datasets without rework. Success is defined by pipelines that run reliably and can be maintained by others.
Deployment, Monitoring & MLOps
- The role deploys Bayesian and machine learning models to production and evaluates their results over time, acting on model drift, data quality issues, calibration and performance degradation. The data scientist establishes monitoring and evaluation routines appropriate to each product, including checks on posterior stability where relevant. Ongoing evaluation protects clients from silently degrading outputs. Success is demonstrated by early detection and resolution of production issues.
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Advanced Modelling & Innovation
- The individual creatively addresses issues with models beyond basic feature engineering, drawing on in-depth knowledge across Bayesian, statistical and machine learning techniques. This includes confidently and independently applying methods they are not yet familiar with when the problem demands it. This depth allows product features to advance rather than remain limited to established methods. Success is measured by measurable improvements in model performance and new capabilities added to products.
Continuous Improvement
- The role critically evaluates where improvements are required in our products and ways of working, always through the perspective of business benefit to maximise value delivered. The data scientist proposes and implements changes to processes, tooling and product features. This keeps the team efficient and the products competitive. Success is demonstrated by improvements adopted across the team with clear impact on quality or delivery time.
Stakeholder & Project Management
- The individual independently manages internal stakeholders and expectations with minimal assistance, including Product, Market Research and Data Engineering teams, and manages reasonably large projects with multiple stakeholders. This involves setting timelines, communicating trade-offs and explaining probabilistic outputs in commercially meaningful terms. Effective management prevents scope drift and delivery surprises. Success is measured by projects delivered on time with stakeholders informed throughout.
Mentorship & Team Enablement
- The role guides more junior members of the team in Bayesian modelling, model development, code quality and deployment, and consistently takes steps to support their growth and success. This is delivered through code reviews, sharing techniques and helping others manage their own projects independently. Building team capability multiplies the impact of the role. Success is demonstrated by the increasing independence and output quality of junior teammates.
Technical Communication & Reporting
- The individual uses Python, SQL and Tableau to produce reporting and analysis, and presents technical work clearly to internal teams and clients, including communicating uncertainty and credible intervals in an accessible way. Requirements and methods are documented to a standard others can build on. Strong communication ensures products are understood and trusted. Success is measured by presentations and documentation that require minimal clarification.
Qualifications
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You'll typically have 5 or more years' experience in data science, with a proven track record of delivering data science products into production independently and of guiding more junior colleagues to do the same. We'd expect a bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, or another highly quantitative field, but we care far more about what you can do than what your degree certificate says. If you came to data science by a different route and can demonstrate the depth we describe here, we want to hear from you. A master's or PhD in a quantitative discipline is a plus, as is an equivalent practical academic foundation. Bayesian statistics will be a genuine area of depth for you.
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You'll be fluent in hierarchical models, MCMC and approximate inference, prior elicitation, and model checking and comparison, and you'll have substantial hands-on experience in PyMC, Stan, NumPyro or similar. Beyond that, you'll have in-depth knowledge across many statistical and machine learning techniques and the confidence to pick up and correctly apply algorithms you haven't used before. You'll know how to deploy, monitor and evaluate models in production, and you'll bring solid software engineering habits: version control, testing, code review and reproducible pipelines.
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You'll be genuinely proficient in Python, SQL and pandas. Experience with Polars is a strong plus. You'll have proven experience with cloud platforms, ideally AWS, though Azure or GCP experience is also relevant.
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On the softer side, you'll be able to understand a business problem, draw conclusions from data and recommend actions, and you'll manage stakeholders and projects independently. You'll communicate and present well, document your work thoroughly and collaborate effectively across teams and time zones. An appreciation of quantitative market research techniques is helpful but not necessary. Experience with TV viewing or advertising data, or the media industry more broadly, isn't a prerequisite, but a genuine interest is. Above all, you'll be curious about data and enjoy building visualisations that clearly articulate insight.
Who We Are
MarketCast measures what moves people. Powered by AI, trained on decades of verified consumer response data, along with proprietary technology, MarketCast is the marketing effectiveness partner for the world's most ambitious brands, connecting emotional resonance to real-world business outcomes across advertising, sponsorships, content, brand health, and advanced analytics. With category-deep expertise spanning sports, entertainment, travel, automotive, and consumer brands, our team of seasoned industry strategists brings unmatched human intelligence to every engagement, turning complex data into decisive action so brands know exactly what's working, why it's working, and what to do next.
At MarketCast, we don't just accept difference - we embrace it, support it, and thrive on it for the benefit of our global culture and success. MarketCast is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know. Check us out on LinkedIn and at: www.marketcast.com
Our Values
Move The Needle
- We turn insight into action and action into impact, acting with urgency and intention at every level, anticipating what's needed, executing with discipline, and driving outcomes that are measured and meaningful.
Client Success is Our Scoreboard
- We win when our clients win. Delivering scaled, tech-enabled solutions that drive measurable impact and value to build lasting partnerships.
Grow Every Day
- Expertise is earned continuously by investing in our craft, our colleagues, and our company.
Own It
- Take responsibility for outcomes, not just outputs.
Benefits
- Free movie tickets!
- 29 days annual leave PLUS Bank Holidays
- Flextime with core hours between 10am and 3pm
- 2 days’ work from home, per week
- 4% match pension scheme
- Enhanced maternity pay
- Regular social events in both UK locations
- Professional growth and career development including LinkedIn Learning
In addition to your salary, MarketCast believes in providing a competitive total rewards package for its employees. All benefits are subject to eligibility requirements, and the terms of our official plan may be modified or amended from time to time.
GENERAL DATA PROTECTION REGULATION (UK GDPR) NOTICE
- The organisation collects and processes personal data relating to its applicants, employees and former employees to manage pre-employment and employment relationships and post-
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