Our leading Healthcare client are looking for a Senior Data analyst to join their team on a contract basis(Inside IR35).
This is a remote role but you must be based in the UK and must have related experience acquired in the Healthcare / Life Sciences sector.
The Sr. Data Analyst will work on a variety of Real-World Evidence (RWE) studies from analyses of existing data to data collected prospectively. The role executes advanced analytics modelling based on exploratory data analysis from complex and high-dimensional datasets through application of statistics, machine learning, programming, data modelling, simulation, and/or advanced mathematics to recognize patterns, identify opportunities, and generate valuable predictive business insights in support of innovative business decisions. Supports organization leadership through data-driven decision validation and support. Programming primarily in R/R Studio but knowledge of SAS or Python is acceptable.
Key Duties and Responsibilities:
- Effectively collaborates with cross-functional stakeholders to identify questions and business challenges and determine plans of action to effectively define, design, and develop machine learning models and algorithms to derive insights into each problem.
- Generates and tests hypotheses and analyzes and interprets the results.
- Navigates large, complex datasets for data mining, profiling, and curation, and natural language processing (NLP), as well as identifies related data that is fundamental to successfully applying predictive and machine learning techniques.
- Designs, develops and programs methods, processes, and software programs to consolidate, cleanse, and analyze unstructured, diverse data sources to recognize patterns, identify opportunities, and generate actionable business insights and solutions.
- Designs, develops, and evaluates predictive models and algorithms that lead to optimal value extraction from the data in order to support business process improvements and solve business challenges.
- Identifies meaningful insights from large data and metadata sources in support of continuous improvement efforts and business process upgrades through exploratory data analysis.
- Effectively communicates and guides stakeholders through the machine learning process; Interprets and communicates findings and solutions from analysis and experiments to a broad audience, including business leadership.
Education, Professional Skills & Experience:
- PhD / MSc with significant related experience or 3-5+ years of relevant working experience
- Candidates from a purely clinical programming background will be considered if they have the soft skills required (creativity, adaptability, problem-solving ability, enjoys a challenge)
- Strong working knowledge of application of statistics, R/R Studio programming, data modelling, simulation, and advanced mathematics to business questions for data analysis
- Demonstrated skills with exploratory data analysis techniques involving structured and unstructured data, machine learning (e.g. decision trees, neural networks, clustering, classification, Bayesian networks), model validation techniques, and data visualization techniques
- In depth knowledge in one or more of the following technical areas: Snowflake, AWS EMR, Python, Spark, R, Shiny, jupyter, and associated packages and libraries from NumPy, pandas, SciPy or NLTK
- Thorough knowledge of and exposure to cloud architectures across NoSQL, lambda functions, kafka, sagemaker, tensorflow, etc.
- Proficiency with the following data science approaches: data engineering, pipelining and wrangling tools, data visualization and modelling tools, and mathematical approaches to imperfect data
- Substantial knowledge of data management approaches such as relational databases, data schemas, object stores, column stores, triple stores, graph stores, and/or document stores
- Proven ability to deliver accurate work products in a cross-functional matrix environment spanning data warehousing, data modelling, and data analytics while managing multiple competing priorities
For further information please contact Nitin on +44 (0)202 255 6655 or via email at email@example.com