Data Scientist

£50K-£55K

Once a week in the Midlands

My Client is on the search for a Data Scientist to join their growing team.

Key Responsibilities:

To design, develop and deploy predictive and prescriptive models using advanced statistical, mathematical, simulation, and machine learning approaches. Build predictive models of demand, lapse, cross-sell, upsell, as well pricing optimisation models, supporting the wider pricing strategy and roadmap Develop, build and deploy strategic pricing initiatives, as well as tactical solutions as needed, to quickly and effectively address trading challenges and realise commercial opportunities Collaborate with wider teams across (e.g.) Protection, Distribution, Product. Actively support the delivery of commercial pricing models and initiatives, aligned to wider business priorities To develop, deploy and automate sophisticated analytical processes and models, informed by structured and unstructured data, to support efficiency and growth initiatives - driving value in pricing models and across all business areas To clean and process data and MI, informing own and team's models and analysis Focussed on adding value through modelling future business data requirements and identifying and quantifying data value

Key Requirements:

Very strong machine learning capability, including:

- Programming: data structures (stacks, queues, multi-dimensional arrays, trees, graphs, etc.), algorithms (searching, sorting, optimization, dynamic programming, etc.)

- Data modelling: finding useful patterns (correlations, clusters, eigenvectors, etc.) and/or predicting properties of previously unseen instances (classification, regression, etc.)

- Data structures: e.g. vectors, matrices, arrays, factors, lists, data frames

- Model evaluation: e.g. validation accuracy, precision, recall, F1-score, MCC, MAE, MAPE, RMSE, PCC

- Functions: built-in functions, User-Defined Functions (UDFs)

- Application of ML algorithms and libraries: identification of a suitable model (e.g. decision tree, nearest neighbour, neural network, SVM, etc.), selecting a learning procedure to fit the data (e.g. linear regression, gradient descent, genetic algorithms, bagging, boosting), controlling for bias and variance, overfitting and underfitting, missing data, data leakage, among others

Solid mathematical knowledge, including:

- Basis of algebra: matrices and linear algebra, algebra of sets

-Probability: theories (conditional probability, Bayes rule, likelihood, independence) and techniques (Naive Bayes, Gaussian Mixture Models, Hidden Markov Models)

- Statistics: (descriptive: mean, median, range, SD, var, analysis of variance: ANOVA, MANOVA, ANCOVA, MANCOVA); Multiple regression, time-series, cross-sectional; Multivariate techniques: PCA and factor analysis)

- Stochastic Processes: Markov chains, queuing processes; Poisson processes, random walks

If interested, send your CV to

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