Data Science Instructor, Principal Data Scientist - New York
Compensation: $94,330.00 - $148,460.00 /year *
Employment Type: Full-Time
Industry: Information Technology
Vision and Values:
Galvanize is a dynamic learning community for technology. Our community is where people and companies with the guts and smarts to create real-world change congregate and inspire each other. Our goal is to make opportunities in technology available to all those with the aptitude, determination, and drive. Across our eight beautifully designed urban campuses, we offer a unique combination of education, workspace, and networking. Galvanize campuses are not simply places to work; they are environments of active engagement, learning, and growth.
Our Data Science Instructors train technical professionals with programming experience to solve data-science problems utilizing innovative educational techniques. We are looking for passionate educators and practical problem solvers with demonstrated flexibility and curiosity. Join us in building the world''s hub for education in data science and data engineering.
As a Data Science instructor at Galvanize, you will:
- Deliver lectures and tutorials on scientific Python, SQL, probability, statistics (Bayesian and frequentist), machine learning, and data engineering.
- Lead day-long ?sprints,? maintaining a strong presence in the classroom and managing other instructional staff.
- Deftly and patiently field student questions and provide feedback in lectures and office hours.
- Build and refine the data-science curriculum and assignments.
- Utilize student feedback and experimentation to continuously improve teaching and assessment methods.
- Evaluate new tools, packages, and tutorials for use in the curriculum.
- Contribute to local evangelism, admissions, and nurture activities, such as attendance at meetups, speaking at conferences, leading workshops (day time and/or evening), etc.
At Galvanize, we strive to provide meaningful professional development opportunities to all of our employees. Here are just a few of the ways you will continue to grow and ?level-up? as a data scientist and a teacher:
- There?s no better way to learn than to teach! You?ll be amazed at how much you?ll develop skills you thought you were already an expert in just by helping students and planning lessons.
- Opportunities to work with and learn from other data-science and web development instructors in a highly collaborative and intellectually rich environment.
- Grow knowledge by learning expanding areas of data science and incorporating them into our curriculum, conducting local improvements and experiments before sharing with other campuses.
- Become the best instructor you can be with ongoing training and support.
- Multiple years of experience in industry in a Data Scientist or Software Engineer role
- Master''s or PhD in a quantitative discipline such as engineering, statistics, or mathematics
- Strong understanding in the topics we teach: scientific Python, probability, statistics (Probability, A/B Testing, Bayesian methods, Regression methods, Time Series), SQL, Machine Learning (Decision Trees, Random Forest, Boosting, Support Vector Machines, Clustering, Natural Language Processing, Recommenders, Graphs), Data Engineering (Hadoop, Hive, and MapReduce), Data Visualization (d3), and data at scale
- Outstanding communication skills
- Multiple years of experience teaching a quantitative subject strongly preferred
- For more details on the program go to:
Galvanize provides equal employment opportunities (EEO) to all employees and applicants for employment. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status.
Associated topics: data analytic, data architect, data center, data integrity, data quality, data scientist, database, database administrator, mongo database administrator, teradata
* The salary listed in the header is an estimate based on salary data for similar jobs in the same area. Salary or compensation data found in the job description is accurate.
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