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Data Science Immersive Instructor (3 Months Contract)

The Role

We are continuously looking for experts in Data Science to become part of our expert pool of immersive instructors. By joining us, you’ll have the opportunity to share your knowledge with others, develop your mentorship and teaching skills, build your personal brand and join a global community of Data Science Immersive experts.

For our immersive programmes, you are required to be available for the duration of the immersive, 3 months, Monday to Friday, 9am -5pm.

Perks

• Flexible Scheduling - Choose a timing that works for you
• On-Going Training - Opportunity to train, research, and learn in the field of software engineering
• Global Connection - Collaborate with a growing network of fellow instructors and active Software Engineering communities
• Competitive pay - Opportunity to earn an additional income at an industry competitive rate

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Key Responsibilities

You will be responsible for both the in-person and virtual community strategy. You will be responsible for the following:

• Data Science Fundamentals - Python programming concepts, the uses of GitHub and other leverage programming tools, solving coding challenges, running Python functions, and key math concepts
• Fundamentals - essential data science tools (e.g., Git, UNIX), navigate data sources and collections via Python and NumPy, utilizing UNIX commands, track changes and iterations, Define and apply descriptive statistical fundamentals, plotting and visualizing data through Python libraries (eg; Matplotlib, Seaborn)
• Exploratory Data Analysis - Perform exploratory data analysis, generate visual and statistical analyses, using Python and its associated libraries, use Pandas to read, clean, parse, and plot data, extracting and rearranging data through indexing, grouping, and JOINing review statistical testing concepts (p values, confidence intervals, lambda functions, correlation/causation) with SciPy and StatsModels, apply to scrape website data using popular scraping tools, bootstrapping, resampling and building inferences
• Classical Statistical Modelling - Model evaluation and optimization, implementing linear and logistic regression, and classification models, connect external data to add nuance to your models using web scraping and APIs, usage of scikit-learn and StatsModels to run linear and logistic regression models and evaluate model fit, classification models by implementing the k-nearest neighbors (kNN) algorithm, optimization and regularization for fitting and tuning models, math and theory behind how gradient descent helps to optimize loss functions for machine learning models
• Machine Learning Models - Knowledge on build machine learning models, supervised and unsupervised learning via clustering, natural language processing, and neural networks, clustering classification models, evaluate ensemble models using decision trees, random forests, bagging, and boosting, natural language processing (NLP) through sentiment analysis of scraped website data, Naive Bayes, use of Hadoop, Hive, Spark’s, ARIMA model
• Advanced Topics and Trends - Knowledge on recommender systems, neural networks, and computer vision models, implementing and productizing models, able to compare and contrast different types of neural networks and demonstrate how they are fit with backpropagation