Data Scientist

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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