Senior Data Scientist: Python, R, Modelling, Forecasting, Databricks
Role is initially working from Home
Senior Data Scientist
Senior Data scientist required to work with the GSK Data Science team to support on TIM initiatives.
We have several initiatives around sales, consumption and promotions. We need 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 by GSK and our partners to augment the Comex team?ability to?understand the effect of promotional activities on orders, consumption and revenue.
The data scientist will build, test and deploy?Machine Learning Solutions.
The projects will be productionised as part of the model’s pipeline. He will be working with our Principal Data Scientist for Sales and Marketing.
• Contribute to all aspects of the Data Science Project Lifecycle from scope through to production
• Provide help 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
• Proven extensive experience developing and implementing Machine Learning algorithms on large data sets
• Be an expert in R and Python programming for Data Science
• Be an expert 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. Particularly time series forecasting.
• Have in depth understanding of statistical modelling / ML techniques for time series forecasting (ARIMA, ETS, Prophet, TS pattern detection, and ML methods)
• Strong experience with Causal inference, Intervention analysis and Scenarios simulation.
• Have a good working knowledge cloud-based data science frameworks and toolkits. Working knowledge of Azure and Databricks is preferred.
• Experience building large scale machine learning systems (e.g. orchestration via Airflow)
• Strong experience in ML lifecycle 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 (e.g. Machine Learning, Optimisation, Applied Mathematics, Simulation, Bayesian Methods etc.), and are a valued and trusted expert