Senior Data Science Manager
Up to £100K!
Once a week in the Midlands
If you're a Senior Data Science Manager looking for a new challenge then this role is for you.
Key Responsibilities:
- To undertake complex analytical processes taking structured and unstructured data, cleaning and processing it to inform and support efficiency and growth initiatives – driving value in pricing models and across all business areas
- To design, develop and deploy predictive and prescriptive models using advanced machine learning, underpinned by developing accuracy and assurance tools
- Focussed on adding value through modelling future business data requirements and identifying and quantifying data value
- To manage and coach a small team
Key Requirements:
- Ability to code in multiple languages but with extensive experience in Python and SQL. Experience with R, Hadoop, Spark, NLP(TK) is desirable.
- Advanced machine learning capability, including:
- Programming: data structures (stacks, queues, multi-dimensional arrays, trees, graphs, etc.), algorithms (searching, sorting, optimization, dynamic programming, etc.), computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.)
- Data modelling: finding useful patterns (correlations, clusters, eigenvectors, etc.) and/or predicting properties of previously unseen instances (classification, regression, anomaly detection, etc.)
- Model evaluation: e.g. validation accuracy, precision, recall, F1-score, MCC, MAE, MAPE, RMSE, PCC2
- 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
- Advanced 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 valriance: ANOVA, MANOVA, ANCOVA, MANCOVA); Multiple regression, time-series, cross-sectional; Multivariate techniques: PCA and factor analysis)
If interested, send your CV to
Applicants must be located and eligible to work in the UK without sponsorship. Please note, should feedback not be received within 28 days, unfortunately your application has been unsuccessful. In applying for this role, you may be registered on our database so we can contact you about suitable opportunities in future. Your data will be managed in accordance with our Privacy Policy, which can be found on our website. If you would like this job advertisement in an alternative format, please contact MERJE directly.
