Daniel Hough

Solving problems across all business divisions that require complex data modelling and statistical analysis. Proactively identifying opportunities to produce insight and recommendations that help drive the Partnership's success, reliably and expeditiously delivering hypotheses and estimations backed up by subsequent detailed modelling. Managing, coaching and mentoring a team of ~20 data scientists, helping them further their skill sets, creating an efficient and highly regarded team that produces real cost-saving, sales-driving and industry-leading results. As a member of the analytics leadership team and the lead practitioner for data science and mathematics, accountable for ensuring that business requirements for advanced analytics have been met, supporting the delivery of new tools and technology, owning business processes and new automated tools, leading the recruitment of data science professionals, utilising R/Python/SQL to showcase frameworks and POCs, and developing a culture of agile working principles. Exceptional communication skills delivering insight in a compelling and engaging way across all levels of understanding, building long term relationships with stakeholders and business leaders in order to maximise their performance.

Responsible for, and managing a team of data scientists engaged with solving business problems that require complex modelling or statistical analysis using machine learning and data science techniques. Investigating creative opportunities to produce insight and recommendations to drive business success.

Data Scientist and Statistician:- Building and optimising learning algorithms, for targeted PPC marketing. Day to day activities include the production of analytical and machine learning algorithms, data mining, text mining, processing, cleaning, and verifying the integrity of data used for analytics. Other activities include the creation of automated systems, performance monitoring, exploratory data analysis, and visualisation. Ad-hoc analysis, presentations and client liaison. Machine Learning – classification, regression, supervised and unsupervised learning, clustering techniques. Statistics – Regression, inference, hypothesis testing, confidence interval analysis, PCA, dimensionality reduction, advanced calculus. Software and Programming Languages – R, Python, SQL (MySQL, SQL Server), Apache Spark, R Markdown, Excel, VBA, Matlab, Simul8, Maple, LaTeX, R Shiny Web Applications, Bit Bucket / Source Tree version control.