Senior Data Scientist at dipoleDIAMOND

dipoleDIAMOND - We are a Business Solutions and Training firm committed to finding cost-effective solutions for Startups, SMEs and Enterprises. We are “life-learners” passionate about technology and education and always seeking out better ways of solving problems. We believe in the power of technology to transform lives, disrupt industries, solve problems, drive growth, improve efficiency and create value for all stakeholders.

We are recruiting to fill the position below:

Job Position: Senior Data Scientist
Job Location: Edo

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.