Senior Data Scientist at dipoleDIAMOND

Job Description

• As a Senior Data Scientist, you will turn raw data into valuable insights that an organisation needs in order to grow and compete.
• Interpret and analyse data from multiple sources to come up with imaginative solutions to problems.

Responsibilities
As Senior Applied Data Scientist, your responsibilities will be to:

• Work with clients, managers and technical staff to understand business needs and develop technical plans to deliver results.
• Develop, deploy, and implement predictive models and protocols for mining production data sources.
• Appropriately deliver existing analytic methods and tools; applying theory to practice.
• Learn new analytic methods and tools as needed.
• Create analytic models and test hypotheses collaboratively in a rapid-paced work environment to meet client needs.
• Responsible for solution and code quality including providing detailed and constructive design and code reviews
• Help establish standards in machine learning and statistical analysis to ensure consistency in quality across projects and teams
• Lead data science consulting engagements on the ground.

Requirements

• Have a strong foundation in statistics and data analytics with expertise in survey research and statistical modelling.
• Experience with one or more data science toolkits such as R, Python, Matlab, Rapidminer, SAS or SPSS.
• Have experience with traditional data mining tools (SQL, Power BI, OLAP, advanced EXCEL, etc.) and ‘Big Data’ tools/techniques (Hadoop, etc.)
• Have experience with machine learning algorithms and classifiers such as k-NN, Naive Bayes, SVM, Random Forest, Linear Regression, ARIMA, Neural Nets, Deep learning, etc.
• Are familiar with applied statistics such as probability distributions, measures of dispersion and central tendency, hypothesis testing and statistical inferences.
• Possess demonstrable experience of delivering a wide variety of machine learning techniques including classifiers, regression, clustering, decisions trees, neural networks, NLP and ensemble techniques
• Understand data visualization patterns and have experience with one or more data visualization tools such as Tableau, R Shiny, Seaborn, MicroStrategy, SAP BusinessObjects, QlikView, etc.
• Have a keen understanding of the business value perspective on predictive analytics.
• Have experience deploying and monitoring predictive models.