Data Scientist

You as a Data Scientist will:

1. Apply and research on machine learning models for deployment with the goals to derive powerful insights, help solve business problems across multiple departments, and provide recommendations to improve pre-existing data structure.
2. Leverage your knowledge of statistical methods and applications; expertise with data mining tools and techniques; and advanced analytical ability to derive actionable insights from big data.
3. Analyze and dissect large, structured and unstructured datasets to identify meaningful patterns for potential forecasting abilities and insight delivery.
4. Work with cross-departmental teams to identify high-value use scenarios that are amenable to an analytics solution.
5. Actively work with third-party partnerships to identify machine learning opportunities to automate and diversify Ellis Don’s core competency in-house.
6. Communicate and explain complex algorithms and their outputs to business stakeholders in a succinct and coherent manner.
7. Assist data engineers and establish best practices for building machine learning pipelines and shipping predictive models to production.
8. Assist with the creation of informative visualizations that intuitively display large amounts of data and/or complex relationships.

Is this the right role for you?

1. Graduate or post-graduate university degree in Computer Science, Mathematics or Statistics, Engineering or equivalent.
2. 2-4 years of relevant work experience.
3. Extensive experience solving analytical problems using quantitative approaches.
4. Comfort manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources.
5. Experience with very large datasets. Familiarity with relational, SQL, and NoSQL databases.
6. Expert knowledge of statistical analysis tools such as R, Matlab, Python or equivalent. Fluency with scripting languages such as Python or others.
7. Familiarity with basic statistical applications and machine learning algorithms (e.g., linear/logistic regression, SVM, gradient boosting trees, supervised/unsupervised learning, deep learning).