Description
Senior Data scientist - Python/R/ML
6 month minimum contract + extensions
London - Remote work available
My client are a leading international end client looking for a Senior Data Scientist with extensive knowledge within Python, R programming and Machin Learning. They need an additional resource to help with models' developments, forecasting algorithms, intervention analysis and simulations. The overall goal of these projects is to utilise the data captured to augment the team's ability to understand the effect of promotional activities and cannibalization on orders, consumption and revenue.
The Data scientist should have;
- Proven extensive experience developing and implementing Machine Learning algorithms on large data sets
- Strong knowledge in Python and R programming for Data Science and Time Series analysis
- Have in depth understanding of statistical modelling/ML techniques for time series forecasting (ARIMA, ETS, Prophet, Time Series pattern detection, and ML methods)
- Strong experience with Causal inference, Intervention analysis, Counterfactuals Estimation and Scenarios simulation
- Solid experience with Probabilistic Programming and Bayesian Methods
- Experience in Python programming for Data Science (pandas, numpy, sklearn, statsmodels)
- Experience in mining large & very complex data sets using SQL and Spark
- Have in depth understanding of statistical modelling techniques and the mathematical foundations of applied ML and AI algorithms and models
- Have a good working knowledge cloud-based data science frameworks and toolkits. Working knowledge of Azure is preferred
- Strong experience in ML life cycle management. Working knowledge of mlflow is preferred
- Are experienced in Agile methodologies and the hypothesis-driven approach
- Have a deep knowledge of a sufficiently broad area of technical specialism (eg Machine Learning, Optimisation, Applied Mathematics, Simulation, Bayesian Methods etc.), and are a valued and trusted expert
You will be responsible for;
- Contribute to all aspects of the Data Science Project Lifecycle from scope through to production
- Provide leadership and guidance on monitoring ongoing data quality and model performance
- Own and define the key performance indicators (KPIs) and diagnostics to measure performance against business goals
- Compile, integrate, and analyse data from multiple sources to answer business questions
- Conceptualize, formulate, prototype and implement algorithms to solve business problems