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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist I - Product - **Company:** MarketCast UK - **Location:** London, UK (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, Cloud Computing, Information Engineering, Information Leak Prevention, Python (Programming Language), Machine Learning, SQL Databases, Support Vector Machine, Tableau (Software), Feature Engineering, Random Forest, Deep Learning, Model Validation, Pandas, Information Technology, Xgboost, AWS Data Analytics, Feature Selection, Unsupervised Learning - **Published:** September 29, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=944dc22cf3f4d21d ## About the Role Qualifications: You'll typically have 2 to 4 years' experience working in data science and will have managed projects independently, even if under a senior lead. 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 skills, we want to hear from you. A master's in a quantitative social science, business analytics or technical discipline is a plus, as is an equivalent practical academic foundation. Technically, you'll be genuinely proficient in Python, SQL and pandas. Experience with Polars is a strong plus, as we use it increasingly for largescale processing. You'll have a solid grounding in Bayesian statistics, meaning you're comfortable with priors, posterior inference, MCMC and model checking, and you've built real models in at least one probabilistic programming framework such as PyMC, Stan or NumPyro. You'll also know your way around advanced machine learning algorithms, feature engineering and selection, evaluation metrics and cross-validation, and you'll have some familiarity with cloud platforms such as AWS or Azure. Beyond the technical, you'll be able to understand a business problem, draw conclusions from data and recommend actions. You'll communicate clearly, document your work and collaborate well 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. ## Description Role Impact: As our Data Scientist I - Product, you'll be a core contributor in our Data Science Product team. The Data Scientist I - Product plays a handson role in exploring, modelling and delivering the data science products that power MarketCast's media and entertainment analytics. Operating at the intersection of quantitative market research and statistical modelling, this position focuses on extracting insight from extremely large datasets, building and validating Bayesian and machine learning models, and maintaining the pipelines that turn those models into reliable client outputs. Bayesian methods are central to how the team quantifies uncertainty, incorporates prior knowledge and models hierarchical audience data, and the data scientist is expected to work confidently within that framework. On a daily basis, the data scientist scopes approaches to typical projects, engineers features that address business problems, and discusses technical aspects of the work with internal teams and clients. By producing robust, well-calibrated models and well-maintained products, this individual directly strengthens the accuracy and repeatability of the organisation's analytics. We're Looking For: Data Exploration & Extraction - The individual explores and extracts data from multiple sources, including TV viewing, advertising and survey data, and interrogates it to create meaningful insights. This is performed in Python and SQL on our AWS data platform, using pandas and increasingly Polars for large-scale processing. Thorough exploration surfaces data quality issues and opportunities before they reach modelling, protecting downstream products from flawed inputs. Success is defined by insights that are accurate, well documented and directly usable by the product and research teams. Project Scoping & Approach Definition - The role scopes and defines the analytical approach for the typical projects the Data Science Product team works on. The data scientist translates a business or product requirement into a clear technical plan covering data needs, methods and deliverables, and identifies where a Bayesian formulation is the right choice. Sound scoping keeps projects on time and prevents rework late in delivery. Success is measured by approaches that are agreed upfront and hold through to completion with minimal revision. Bayesian Modelling - The individual builds, fits and validates Bayesian models such as hierarchical regressions, Bayesian time series and probabilistic audience models using frameworks like PyMC, Stan or NumPyro. This involves specifying sensible priors, checking convergence diagnostics and running posterior predictive checks to assess fit. Bayesian modelling allows the team to quantify uncertainty honestly and combine sparse data with prior knowledge, which is essential for outputs used in commercial decisions. Success is demonstrated by models that are well calibrated, interpretable and communicated in terms of credible intervals rather than point estimates alone. Machine Learning Development - The role researches, develops and tests solutions using both supervised and unsupervised learning, including forecasting, segmentation, random forests, gradient boosting and support vector machines. The data scientist maintains broad awareness of the techniques available for each problem type and in-depth knowledge in a couple of areas. This breadth allows the right method to be selected rather than the most familiar one. Success is demonstrated by models that meet agreed evaluation criteria and are appropriate to the product context. Feature Engineering & Model Robustness - The individual thinks critically about what features are required to address a client business problem and transforms the data to achieve this. This is executed through feature selection techniques, cross-validation and appropriate evaluation metrics, with vigilance against issues such as data leakage. Robust models protect the credibility of product outputs delivered to high-profile clients. Success is defined by models that senior team members can rely on without extensive re-checking. Pipeline & Product Maintenance - The role maintains and improves existing machine learning and statistical pipelines, processes and products. The data scientist monitors outputs, fixes defects and implements incremental enhancements in collaboration with the India-based Data Engineering team. Stable pipelines ensure recurring deliverables reach clients on schedule and with consistent quality. Success is measured by reduced pipeline failures and improvements delivered without disrupting production. Insight Visualisation & Reporting - The individual creates bespoke client reports and data visualisations using Tableau and Python plotting libraries, including clear representation of uncertainty from Bayesian outputs. Model results are converted into insight-led charts that follow brand style guides. Effective visualisation lets researchers and clients act on findings quickly. Success is demonstrated by reports that require little clarification and are reused as templates across projects. Technical Communication & Collaboration - The role discusses the technical and analytical aspects of the work with clients and with the Market Research, Product and Data Engineering teams across the UK, US and India. The data scientist documents requirements, explains Bayesian and ML methods at an appropriate level of detail and participates in internal knowledge-sharing sessions. Clear communication builds trust in the products and reduces misunderstandings on deliverables. Success is measured by stakeholder confidence in the methodology and effective coordination across time zones. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Shoot for the moon - machine learning for automated online ad detection](https://www.wearedevelopers.com/videos/502-shoot-for-the-moon-machine-learning-for-automated-online-ad-detection) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) ## Related Articles - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [Jobs in Tech: The State of the European Market](https://www.wearedevelopers.com/magazine/575-jobs-in-tech-the-state-of-the-european-market) - [Data Analyst Salary Germany](https://www.wearedevelopers.com/magazine/277-data-analyst-salary-germany) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)